%% Papers Published
@article{fds376060,
Author = {Chew, J and Hirn, M and Krishnaswamy, S and Needell, D and Perlmutter,
M and Steach, H and Viswanath, S and Wu, HT},
Title = {Geometric scattering on measure spaces},
Journal = {Applied and Computational Harmonic Analysis},
Volume = {70},
Year = {2024},
Month = {May},
url = {http://dx.doi.org/10.1016/j.acha.2024.101635},
Abstract = {The scattering transform is a multilayered, wavelet-based
transform initially introduced as a mathematical model of
convolutional neural networks (CNNs) that has played a
foundational role in our understanding of these networks'
stability and invariance properties. In subsequent years,
there has been widespread interest in extending the success
of CNNs to data sets with non-Euclidean structure, such as
graphs and manifolds, leading to the emerging field of
geometric deep learning. In order to improve our
understanding of the architectures used in this new field,
several papers have proposed generalizations of the
scattering transform for non-Euclidean data structures such
as undirected graphs and compact Riemannian manifolds
without boundary. Analogous to the original scattering
transform, these works prove that these variants of the
scattering transform have desirable stability and invariance
properties and aim to improve our understanding of the
neural networks used in geometric deep learning. In this
paper, we introduce a general, unified model for geometric
scattering on measure spaces. Our proposed framework
includes previous work on compact Riemannian manifolds
without boundary and undirected graphs as special cases but
also applies to more general settings such as directed
graphs, signed graphs, and manifolds with boundary. We
propose a new criterion that identifies to which groups a
useful representation should be invariant and show that this
criterion is sufficient to guarantee that the scattering
transform has desirable stability and invariance properties.
Additionally, we consider finite measure spaces that are
obtained from randomly sampling an unknown manifold. We
propose two methods for constructing a data-driven graph on
which the associated graph scattering transform approximates
the scattering transform on the underlying manifold.
Moreover, we use a diffusion-maps based approach to prove
quantitative estimates on the rate of convergence of one of
these approximations as the number of sample points tends to
infinity. Lastly, we showcase the utility of our method on
spherical images, a directed graph stochastic block model,
and on high-dimensional single-cell data.},
Doi = {10.1016/j.acha.2024.101635},
Key = {fds376060}
}
@article{fds374249,
Author = {Chung, YM and Huang, WK and Wu, HT},
Title = {Topological data analysis assisted automated sleep stage
scoring using airflow signals},
Journal = {Biomedical Signal Processing and Control},
Volume = {89},
Pages = {105760-105760},
Publisher = {Elsevier BV},
Year = {2024},
Month = {March},
url = {http://dx.doi.org/10.1016/j.bspc.2023.105760},
Abstract = {Objective: Breathing pattern variability (BPV), as a
universal physiological feature, encodes rich health
information. We aim to show that, a high-quality automatic
sleep stage scoring based on a proper quantification of BPV
extracting from the single airflow signal can be achieved.
Methods: Topological data analysis (TDA) is applied to
characterize BPV from the intrinsically nonstationary
airflow signal. The extracted features from TDA are utilized
to train an automatic sleep stage scoring model using the
XGBoost learner. Additionally, the noise and artifacts that
are typically present in the air flow signal are leveraged
to improve the performance of the trained system. To
evaluate the effectiveness of the proposed approach, a
state-of-the-art method is implemented for comparison
purposes. Results: A leave-one-subject-out cross-validation
was conducted on a dataset comprising 30 whole-night
polysomnogram signals with standard annotations. The results
show that the proposed features outperform those considered
in the state-of-the-art work in terms of overall accuracy
(78.8% ± 8.7% vs. 75.0% ± 9.6%) and Cohen's kappa (0.56 ±
0.15 vs. 0.50 ± 0.15) for automatically scoring wake, rapid
eye movement (REM), and non-REM (NREM) stages. An external
validation conducted on a dataset comprising 80 whole-night
polysomnogram signals with standard annotations shows a
result of overall accuracy 74.1%±11.6% and Cohen's kappa
0.42±0.15, which again outperforms the state-of-the-art
work. Furthermore, the analysis of feature importance
reveals that the TDA features provide complementary
information to the traditional features commonly used in the
literature, and the respiratory quality index is identified
as an essential component. Conclusion: The proposed
TDA-assisted automatic annotation system can accurately
distinguish wake, REM and NREM from the airflow signal.
Significance: The utilization of a single air flow channel
and the universality of BPV suggest the potential of
TDA-assisted signal processing in addressing various
biomedical signals and homecare issues beyond sleep stage
annotation.},
Doi = {10.1016/j.bspc.2023.105760},
Key = {fds374249}
}
@article{fds375271,
Author = {Liu, T-C and Chen, Y-C and Chen, P-L and Tu, P-H and Yeh, C-H and Yeap,
M-C and Wu, Y-H and Chen, C-C and Wu, H-T},
Title = {Removal of electrical stimulus artifact in local field
potential recorded from subthalamic nucleus by using
manifold denoising.},
Journal = {Journal of neuroscience methods},
Volume = {403},
Pages = {110038},
Year = {2024},
Month = {March},
url = {http://dx.doi.org/10.1016/j.jneumeth.2023.110038},
Abstract = {<h4>Background</h4>Deep brain stimulation (DBS) is an
effective treatment for movement disorders such as
Parkinson's disease (PD). However, local field potentials
(LFPs) recorded through lead externalization during
high-frequency stimulation (HFS) are contaminated by
stimulus artifacts, which require to be removed before
further analysis.<h4>New method</h4>In this study, a novel
stimulus artifact removal algorithm based on manifold
denoising, termed Shrinkage and Manifold-based Artifact
Removal using Template Adaptation (SMARTA), was proposed to
remove artifacts by deriving a template for each stimulus
artifact and subtracting it from the signal. Under a
low-dimensional manifold assumption, a matrix denoising
technique called optimal shrinkage was applied to design a
similarity metric such that the template for stimulus
artifacts could be accurately recovered.<h4>Result</h4>SMARTA
was evaluated using semirealistic signals, which were the
combination of semirealistic stimulus artifacts recorded in
an agar brain model and LFPs of PD patients with no
stimulation, and realistic LFP signals recorded in patients
with PD during HFS. The results indicated that SMARTA
removes stimulus artifacts with a modest distortion in LFP
estimates.<h4>Comparison with existing methods</h4>SMARTA
was compared with moving-average subtraction,
sample-and-interpolate technique, and Hampel
filtering.<h4>Conclusion</h4>The proposed SMARTA algorithm
helps the exploration of the neurophysiological mechanisms
of DBS effects.},
Doi = {10.1016/j.jneumeth.2023.110038},
Key = {fds375271}
}
@article{fds375222,
Author = {Ding, X and Wu, HT},
Title = {How do kernel-based sensor fusion algorithms behave under
high-dimensional noise?},
Journal = {Information and Inference},
Volume = {13},
Number = {1},
Publisher = {Oxford University Press (OUP)},
Year = {2024},
Month = {March},
url = {http://dx.doi.org/10.1093/imaiai/iaad051},
Abstract = {We study the behavior of two kernel based sensor fusion
algorithms, nonparametric canonical correlation analysis
(NCCA) and alternating diffusion (AD), under the nonnull
setting that the clean datasets collected from two sensors
are modeled by a common low-dimensional manifold embedded in
a high-dimensional Euclidean space and the datasets are
corrupted by high-dimensional noise. We establish the
asymptotic limits and convergence rates for the eigenvalues
of the associated kernel matrices assuming that the sample
dimension and sample size are comparably large, where NCCA
and AD are conducted using the Gaussian kernel. It turns out
that both the asymptotic limits and convergence rates depend
on the signal-to-noise ratio (SNR) of each sensor and
selected bandwidths. On one hand, we show that if NCCA and
AD are directly applied to the noisy point clouds without
any sanity check, it may generate artificial information
that misleads scientists' interpretation. On the other hand,
we prove that if the bandwidths are selected adequately,
both NCCA and AD can be made robust to high-dimensional
noise when the SNRs are relatively large.},
Doi = {10.1093/imaiai/iaad051},
Key = {fds375222}
}
@article{fds373972,
Author = {Chiu, N-T and Chuang, B and Anakmeteeprugsa, S and Shelley, KH and Alian, AA and Wu, H-T},
Title = {Signal quality assessment of peripheral venous
pressure.},
Journal = {Journal of clinical monitoring and computing},
Volume = {38},
Number = {1},
Pages = {101-112},
Year = {2024},
Month = {February},
url = {http://dx.doi.org/10.1007/s10877-023-01071-9},
Abstract = {Develop a signal quality index (SQI) for the widely
available peripheral venous pressure waveform (PVP). We
focus on the quality of the cardiac component in PVP. We
model PVP by the adaptive non-harmonic model. When the
cardiac component in PVP is stronger, the PVP is defined to
have a higher quality. This signal quality is quantified by
applying the synchrosqueezing transform to decompose the
cardiac component out of PVP, and the SQI is defined as a
value between 0 and 1. A database collected during the lower
body negative pressure experiment is utilized to validate
the developed SQI. All signals are labeled into categories
of low and high qualities by experts. A support vector
machine (SVM) learning model is trained for practical
purpose. The developed signal quality index coincide with
human experts' labels with the area under the curve 0.95. In
a leave-one-subject-out cross validation (LOSOCV), the SQI
achieves accuracy 0.89 and F1 0.88, which is consistently
higher than other commonly used signal qualities, including
entropy, power and mean venous pressure. The trained SVM
model trained with SQI, entropy, power and mean venous
pressure could achieve an accuracy 0.92 and F1 0.91 under
LOSOCV. An exterior validation of SQI achieves accuracy 0.87
and F1 0.92; an exterior validation of the SVM model
achieves accuracy 0.95 and F1 0.96. The developed SQI has a
convincing potential to help identify high quality PVP
segments for further hemodynamic study. This is the first
work aiming to quantify the signal quality of the widely
applied PVP waveform.},
Doi = {10.1007/s10877-023-01071-9},
Key = {fds373972}
}
@article{fds373608,
Author = {Shnitzer, T and Wu, HT and Talmon, R},
Title = {Spatiotemporal analysis using Riemannian composition of
diffusion operators},
Journal = {Applied and Computational Harmonic Analysis},
Volume = {68},
Year = {2024},
Month = {January},
url = {http://dx.doi.org/10.1016/j.acha.2023.101583},
Abstract = {Multivariate time-series have become abundant in recent
years, as many data-acquisition systems record information
through multiple sensors simultaneously. In this paper, we
assume the variables pertain to some geometry and present an
operator-based approach for spatiotemporal analysis. Our
approach combines three components that are often considered
separately: (i) manifold learning for building operators
representing the geometry of the variables, (ii) Riemannian
geometry of symmetric positive-definite matrices for
multiscale composition of operators corresponding to
different time samples, and (iii) spectral analysis of the
composite operators for extracting different dynamic modes.
We propose a method that is analogous to the classical
wavelet analysis, which we term Riemannian multi-resolution
analysis (RMRA). We provide some theoretical results on the
spectral analysis of the composite operators, and we
demonstrate the proposed method on simulations and on real
data.},
Doi = {10.1016/j.acha.2023.101583},
Key = {fds373608}
}
@article{fds371626,
Author = {Wang, S-C and Ting, C-K and Chen, C-Y and Liu, C and Lin, N-C and Loong,
C-C and Wu, H-T and Lin, Y-T},
Title = {Arterial blood pressure waveform in liver transplant surgery
possesses variability of morphology reflecting recipients'
acuity and predicting short term outcomes.},
Journal = {Journal of clinical monitoring and computing},
Volume = {37},
Number = {6},
Pages = {1521-1531},
Year = {2023},
Month = {December},
url = {http://dx.doi.org/10.1007/s10877-023-01047-9},
Abstract = {We investigated clinical information underneath the
beat-to-beat fluctuation of the arterial blood pressure
(ABP) waveform morphology. We proposed the Dynamical
Diffusion Map algorithm (DDMap) to quantify the variability
of morphology. The underlying physiology could be the
compensatory mechanisms involving complex interactions
between various physiological mechanisms to regulate the
cardiovascular system. As a liver transplant surgery
contains distinct periods, we investigated its clinical
behavior in different surgical steps. Our study used DDmap
algorithm, based on unsupervised manifold learning, to
obtain a quantitative index for the beat-to-beat variability
of morphology. We examined the correlation between the
variability of ABP morphology and disease acuity as
indicated by Model for End-Stage Liver Disease (MELD)
scores, the postoperative laboratory data, and 4 early
allograft failure (EAF) scores. Among the 85 enrolled
patients, the variability of morphology obtained during the
presurgical phase was best correlated with MELD-Na scores.
The neohepatic phase variability of morphology was
associated with EAF scores as well as postoperative
bilirubin levels, international normalized ratio, aspartate
aminotransferase levels, and platelet count. Furthermore,
variability of morphology presents more associations with
the above clinical conditions than the common BP measures
and their BP variability indices. The variability of
morphology obtained during the presurgical phase is
indicative of patient acuity, whereas those during the
neohepatic phase are indicative of short-term surgical
outcomes.},
Doi = {10.1007/s10877-023-01047-9},
Key = {fds371626}
}
@article{fds371627,
Author = {Eid, A-M and Elgamal, M and Gonzalez-Fiol, A and Shelley, KH and Wu,
H-T and Alian, AA},
Title = {Using the ear photoplethysmographic waveform as an early
indicator of central hypovolemia in healthy volunteers
utilizing LBNP induced hypovolemia model.},
Journal = {Physiological measurement},
Volume = {44},
Number = {5},
Year = {2023},
Month = {July},
url = {http://dx.doi.org/10.1088/1361-6579/acd165},
Abstract = {<i>Objective</i>. To study the photoplethysmographic (PPG)
waveforms of different locations (ear and finger) during
lower body negative pressure (LBNP) induced hypovolemia.
Then, to determine whether the PPG waveform can be used to
detect hypovolemia during the early stage of
LBNP.<i>Approach</i>. 36 healthy volunteers were recruited
for progressive LBNP induced hypovolemia, with an endpoint
of -60 mmHg or development of hypoperfusion symptoms,
whichever comes first. Subjects tolerating the entire
protocol without symptoms were designated as high tolerance
(HT), while symptomatic subjects were designated as low
tolerance (LT). Subjects were monitored with an
electrocardiogram, continuous noninvasive blood pressure
monitor, and two pulse oximetry probes, one on the ear
(Xhale) and one the finger (Nellcor). Stroke volume was
measured non-invasively utilizing Non-Invasive Cardiac
Output Monitor (NICOM, Cheetah Medical). The waveform
morphology was analyzed using novel PPG waveforms indices,
including phase hemodynamic index (PHI) and amplitude
hemodyamaic index and were evaluated from the ear PPG and
finger PPG at different LBNP stages.<i>Main results</i>. The
PHI, particularly the phase relationship between the second
harmonic and the fundamental component of the ear PPG
denoted as∇φ2,during the early stage of LBNP (-15 mmHg)
in the HT and LT groups is statistically significantly
different (<i>p</i>value = 0.0033) with the area under curve
0.81 (CI: 0.616-0.926). The other indices are not
significantly different. The 5 fold cross validation shows
that∇φ2during the early stage of LBNP (-15 mmHg) as the
single index could predict the tolerance of the subject with
the sensitivity, specificity, accuracy and<i>F</i>1 as 0.771
± 0.192, 0.71 ± 0.107, 0.7 ± 0.1 and 0.771 ± 0.192
respectively.<i>Significance</i>. The ear's PPG PHI which
compares the phases of the fundamental and second harmonic
has the potential to be used as an early predictor of
central hypovolemia.},
Doi = {10.1088/1361-6579/acd165},
Key = {fds371627}
}
@article{fds371252,
Author = {Wu, H-T and Harezlak, J},
Title = {Application of de-shape synchrosqueezing to estimate gait
cadence from a single-sensor accelerometer placed in
different body locations.},
Journal = {Physiological measurement},
Volume = {44},
Number = {5},
Year = {2023},
Month = {May},
url = {http://dx.doi.org/10.1088/1361-6579/accefe},
Abstract = {<i>Objective.</i>Commercial and research-grade wearable
devices have become increasingly popular over the past
decade. Information extracted from devices using
accelerometers is frequently summarized as 'number of steps'
(commercial devices) or 'activity counts' (research-grade
devices). Raw accelerometry data that can be easily
extracted from accelerometers used in research, for instance
ActiGraph GT3X+, are frequently discarded.<i>Approach.</i>Our
primary goal is proposing an innovative use of
the<i>de-shape synchrosqueezing transform</i>to analyze the
raw accelerometry data recorded from a single sensor
installed in different body locations, particularly the
wrist, to extract<i>gait cadence</i>when a subject is
walking. The proposed methodology is tested on data
collected in a semi-controlled experiment with 32
participants walking on a one-kilometer predefined course.
Walking was executed on a flat surface as well as on the
stairs (up and down).<i>Main results.</i>The cadences of
walking on a flat surface, ascending stairs, and descending
stairs, determined from the wrist sensor, are 1.98 ± 0.15
Hz, 1.99 ± 0.26 Hz, and 2.03 ± 0.26 Hz respectively. The
cadences are 1.98 ± 0.14 Hz, 1.97 ± 0.25 Hz, and 2.02 ±
0.23 Hz, respectively if determined from the hip sensor,
1.98 ± 0.14 Hz, 1.93 ± 0.22 Hz and 2.06 ± 0.24 Hz,
respectively if determined from the left ankle sensor, and
1.98 ± 0.14 Hz, 1.97 ± 0.22 Hz, and 2.04 ± 0.24 Hz,
respectively if determined from the right ankle sensor. The
difference is statistically significant indicating that the
cadence is fastest while descending stairs and slowest when
ascending stairs. Also, the standard deviation when the
sensor is on the wrist is larger. These findings are in line
with our expectations.<i>Conclusion.</i>We show that our
proposed algorithm can extract the cadence with high
accuracy, even when the sensor is placed on the
wrist.},
Doi = {10.1088/1361-6579/accefe},
Key = {fds371252}
}
@article{fds370969,
Author = {Liu, GR and Sheu, YC and Wu, HT},
Title = {CENTRAL AND NONCENTRAL LIMIT THEOREMS ARISING FROM THE
SCATTERING TRANSFORM AND ITS NEURAL ACTIVATION
GENERALIZATION},
Journal = {SIAM Journal on Mathematical Analysis},
Volume = {55},
Number = {2},
Pages = {1170-1213},
Year = {2023},
Month = {April},
url = {http://dx.doi.org/10.1137/21M1454511},
Abstract = {Motivated by the analysis of complicated time series, we
examine a generalization of the scattering transform that
includes broad neural activation functions. This
generalization is the neural activation scattering transform
(NAST). NAST comprises a sequence of "neural processing
units," each of which applies a high pass filter to the
input from the previous layer followed by a composition with
a nonlinear function as the output to the next neuron. Here,
the nonlinear function models how a neuron gets excited by
the input signal. In addition to showing properties like
nonexpansion, horizontal translational invariability, and
insensitivity to local deformation, we explore the
statistical properties of the second-order NAST of a
Gaussian process with various dependence structures and its
interaction with the chosen wavelets and activation
functions. We also provide central limit theorem (CLT) and
non-CLT results. Numerical simulations demonstrate the
developed theorems. Our results explain how NAST processes
complicated time series, paving a way toward statistical
inference based on NAST for real-world applications.},
Doi = {10.1137/21M1454511},
Key = {fds370969}
}
@article{fds367648,
Author = {Wang, YG and Womersley, RS and Wu, HT and Yu, WH},
Title = {Numerical computation of triangular complex spherical
designs with small mesh ratio},
Journal = {Journal of Computational and Applied Mathematics},
Volume = {421},
Year = {2023},
Month = {March},
url = {http://dx.doi.org/10.1016/j.cam.2022.114796},
Abstract = {This paper provides triangular spherical designs for the
complex unit sphere Ωd⊂ℂd by exploiting the natural
correspondence with the real unit sphere S2d−1⊂R2d. A
variational characterization of triangular complex designs
is provided, with particular emphasis on numerical
computation of efficient triangular complex designs with
good geometric properties as measured by their mesh ratio.
We give numerical examples of triangular spherical t-designs
on complex unit spheres of dimension d=2 to
6.},
Doi = {10.1016/j.cam.2022.114796},
Key = {fds367648}
}
@article{fds367604,
Author = {Ding, X and Wu, HT},
Title = {Impact of Signal-to-Noise Ratio and Bandwidth on Graph
Laplacian Spectrum From High-Dimensional Noisy Point
Cloud},
Journal = {IEEE Transactions on Information Theory},
Volume = {69},
Number = {3},
Pages = {1899-1931},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2023},
Month = {March},
url = {http://dx.doi.org/10.1109/TIT.2022.3216561},
Abstract = {We systematically study the spectrum of kernel-based graph
Laplacian (GL) constructed from high-dimensional and noisy
random point cloud in the nonnull setup. The problem is
motived by studying the model when the clean signal is
sampled from a manifold that is embedded in a
low-dimensional Euclidean subspace, and corrupted by
high-dimensional noise. We quantify how the signal and noise
interact in different regions of signal-to-noise ratio
(SNR), and report the resulting peculiar spectral behavior
of GL. In addition, we explore the impact of chosen kernel
bandwidth on the spectrum of GL over different regions of
SNR, which lead to an adaptive choice of kernel bandwidth
that coincides with the common practice in real data. This
result paves the way to a theoretical understanding of how
practitioners apply GL when the dataset is
noisy.},
Doi = {10.1109/TIT.2022.3216561},
Key = {fds367604}
}
@article{fds368886,
Author = {Steinerberger, S and Wu, HT},
Title = {Fundamental component enhancement via adaptive nonlinear
activation functions},
Journal = {Applied and Computational Harmonic Analysis},
Volume = {63},
Pages = {135-143},
Year = {2023},
Month = {March},
url = {http://dx.doi.org/10.1016/j.acha.2022.11.007},
Abstract = {In many real world oscillatory signals, the fundamental
component of a signal f(t) might be weak or does not exist.
This makes it difficult to estimate the instantaneous
frequency of the signal. A traditional approach is to apply
the rectification trick, working with |f(t)| or ReLu(f(t))
instead, to enhance the fundamental component. This raises
an interesting question: what type of nonlinear function
g:R→R has the property that g(f(t)) has a more pronounced
fundamental frequency? g(t)=|t| and g(t)=ReLu(t) seem to
work well in practice; we propose a variant of
g(t)=1/(1−|t|) and provide a theoretical guarantee.
Several simulated signals and real signals are analyzed to
demonstrate the performance of the proposed
solution.},
Doi = {10.1016/j.acha.2022.11.007},
Key = {fds368886}
}
@article{fds364956,
Author = {Alian, A and Shelley, K and Wu, H-T},
Title = {Amplitude and phase measurements from harmonic analysis may
lead to new physiologic insights: lower body negative
pressure photoplethysmographic waveforms as an
example.},
Journal = {Journal of clinical monitoring and computing},
Volume = {37},
Number = {1},
Pages = {127-137},
Year = {2023},
Month = {February},
url = {http://dx.doi.org/10.1007/s10877-022-00866-6},
Abstract = {The photoplethysmographic (PPG) waveform contains
hemodynamic information in its oscillations. We provide a
new method for quantitative study of the waveform morphology
and its relationship to the hemodynamics. A data adaptive
modeling of the waveform shape is used to describe the PPG
waveforms recorded from ear and finger. Several indices,
based on the phase and amplitude information of different
harmonics, are proposed to describe the PPG morphology. The
proposed approach is illustrated by analyzing PPG waveforms
recorded during a lower body negative pressure (LBNP)
experiment. Different phase and amplitude dynamics are
observed during the LBNP experiment. Specifically, we
observe that the phase difference between the high order
harmonics and fundamental components change more
significantly when the PPG signal is recorded from the ear
than the finger at the beginning of the study. In contrast,
the finger PPG amplitude changes more when compared to the
ear PPG during the recovery period. A more complete harmonic
analysis of the PPG appears to provide new hemodynamic
information when used during a LBNP experiment. We encourage
other investigators who possess modulated clinical waveform
data (e.g. PPG, arterial pressure, respiratory, and
autonomic) to re-examine their data, using phase information
and higher harmonics as a potential source of new insights
into underlying physiologic mechanisms.},
Doi = {10.1007/s10877-022-00866-6},
Key = {fds364956}
}
@article{fds365844,
Author = {Chen, Z and Wu, HT},
Title = {Disentangling modes with crossover instantaneous frequencies
by synchrosqueezed chirplet transforms, from theory to
application},
Journal = {Applied and Computational Harmonic Analysis},
Volume = {62},
Pages = {84-122},
Year = {2023},
Month = {January},
url = {http://dx.doi.org/10.1016/j.acha.2022.08.004},
Abstract = {Analysis of signals with oscillatory modes with crossover
instantaneous frequencies is a challenging problem in time
series analysis. One way to handle this problem is lifting
the 2-dimensional time-frequency representation to a
3-dimensional representation, called time-frequency-chirp
rate (TFC) representation, by adding one extra chirp rate
parameter so that crossover frequencies are disentangled in
higher dimension. The chirplet transform is an algorithm for
this lifting idea, which leads to a TFC representation.
However, in practice, we found that it has a strong
“blurring” effect in the chirp rate axis, which limits
its application in real-world data. Moreover, to our
knowledge, we have limited mathematical understanding of the
chirplet transform in the literature. Motivated by the need
for the real-world data analysis, in this paper, we propose
the synchrosqueezed chirplet transform (SCT) that enhances
the TFC representation given by the chirplet transform. The
resulting concentrated TFC representation has high contrast
so that one can better distinguish different modes with
crossover instantaneous frequencies. The basic idea is to
use the phase information in the chirplet transform to
determine a reassignment rule that sharpens the TFC
representation determined by the chirplet transform. We also
analyze the chirplet transform and provide theoretical
guarantees of SCT.},
Doi = {10.1016/j.acha.2022.08.004},
Key = {fds365844}
}
@article{fds369945,
Author = {Colominas, MA and Wu, HT},
Title = {An Iterative Warping and Clustering Algorithm to Estimate
Multiple Wave-Shape Functions From a Nonstationary
Oscillatory Signal},
Journal = {IEEE Transactions on Signal Processing},
Volume = {71},
Pages = {701-712},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2023},
Month = {January},
url = {http://dx.doi.org/10.1109/TSP.2023.3252883},
Abstract = {Nonsinusoidal oscillatory signals are everywhere. In
practice, the nonsinusoidal oscillatory pattern, modeled as
a 1-periodic wave-shape function (WSF), might vary from
cycle to cycle. When there are finite different WSFs, s1,sK,
so that the WSF jumps from one to another suddenly, the
different WSFs and jumps encode useful information. We
present an iterative warping and clustering algorithm to
estimate s1,sK from a nonstationary oscillatory signal with
time-varying amplitude and frequency, and hence the change
points of the WSFs. The algorithm is a novel combination of
time-frequency analysis, singular value decomposition
entropy and vector spectral clustering. We demonstrate the
efficiency of the proposed algorithm with simulated and real
signals, including the voice signal, arterial blood
pressure, electrocardiogram and accelerometer signal.
Moreover, we provide a mathematical justification of the
algorithm under the assumption that the amplitude and
frequency of the signal are slowly time-varying and there
are finite change points that model sudden changes from one
wave-shape function to another one.},
Doi = {10.1109/TSP.2023.3252883},
Key = {fds369945}
}
@article{fds375272,
Author = {Chen, HY and Wu, HT and Chen, CY},
Title = {Quality Aware Sleep Stage Classification over RIP Signals
with Persistence Diagrams},
Journal = {2023 IEEE 19th International Conference on Body Sensor
Networks, BSN 2023 - Proceedings},
Year = {2023},
Month = {January},
ISBN = {9798350338416},
url = {http://dx.doi.org/10.1109/BSN58485.2023.10331130},
Abstract = {Automated sleep stage classification is a valuable tool for
analyzing sleep patterns and has numerous applications in
wearable healthcare systems. However, the accuracy of sleep
stage classification using signals from wearable devices can
be affected by data quality issues such as signal
interference or packet loss. In this study, we present an
algorithm that addresses packet loss in respiratory
inductive plethysmography (RIP) signals for sleep stage
detection. RIP signals can be conveniently collected using
abdominal and thoracic belts. By exploring the rich
structural patterns in such signals, we utilize persistence
diagrams to uncover macro-structures for sleep stage
classification, which is particularly suitable for high data
missing rates. Our model achieves a promising performance of
76% accuracy and a 0.54 Cohen's kappa coefficient for
three-stage classification. Additionally, we evaluate the
model across different missing data rates and highlight the
superior fault tolerance of persistence diagram features
compared to other conventional temporal and spectral
features.},
Doi = {10.1109/BSN58485.2023.10331130},
Key = {fds375272}
}
@article{fds370611,
Author = {Young, AL and van den Boom, W and Schroeder, RA and Krishnamoorthy,
V and Raghunathan, K and Wu, H-T and Dunson, DB},
Title = {Mutual information: Measuring nonlinear dependence in
longitudinal epidemiological data.},
Journal = {PLoS One},
Volume = {18},
Number = {4},
Pages = {e0284904},
Year = {2023},
url = {http://dx.doi.org/10.1371/journal.pone.0284904},
Abstract = {Given a large clinical database of longitudinal patient
information including many covariates, it is computationally
prohibitive to consider all types of interdependence between
patient variables of interest. This challenge motivates the
use of mutual information (MI), a statistical summary of
data interdependence with appealing properties that make it
a suitable alternative or addition to correlation for
identifying relationships in data. MI: (i) captures all
types of dependence, both linear and nonlinear, (ii) is zero
only when random variables are independent, (iii) serves as
a measure of relationship strength (similar to but more
general than R2), and (iv) is interpreted the same way for
numerical and categorical data. Unfortunately, MI typically
receives little to no attention in introductory statistics
courses and is more difficult than correlation to estimate
from data. In this article, we motivate the use of MI in the
analyses of epidemiologic data, while providing a general
introduction to estimation and interpretation. We illustrate
its utility through a retrospective study relating
intraoperative heart rate (HR) and mean arterial pressure
(MAP). We: (i) show postoperative mortality is associated
with decreased MI between HR and MAP and (ii) improve
existing postoperative mortality risk assessment by
including MI and additional hemodynamic statistics.},
Doi = {10.1371/journal.pone.0284904},
Key = {fds370611}
}
@article{fds370866,
Author = {Shen, C and Wu, HT},
Title = {Scalability and robustness of spectral embedding: landmark
diffusion is all you need},
Journal = {Information and Inference},
Volume = {11},
Number = {4},
Pages = {1527-1595},
Year = {2022},
Month = {December},
url = {http://dx.doi.org/10.1093/imaiai/iaac013},
Abstract = {Although spectral embedding is a widely applied dimension
reduction technique in various fields, so far it is still
challenging to make it scalable to handle’big data’. On
the other hand, the robustness property is less explored and
there exists only limited theoretical results. Motivated by
the need of handling such data, recently we proposed a novel
spectral embedding algorithm, which we coined Robust and
Scalable Embedding via Landmark Diffusion (ROSELAND). In
short, we measure the affinity between two points via a set
of landmarks, which is composed of a small number of points,
and’diffuse’ on the dataset via the landmark set to
achieve a spectral embedding. Roseland can be viewed as a
generalization of the commonly applied spectral embedding
algorithm, the diffusion map (DM), in the sense that it
shares various properties of DM. In this paper, we show that
Roseland is not only numerically scalable, but also
preserves the geometric properties via its diffusion nature
under the manifold setup; that is, we theoretically explore
the asymptotic behavior of Roseland under the manifold
setup, including handling the U-statistics-like quantities,
and provide a L∞ spectral convergence with a rate.
Moreover, we offer a high dimensional noise analysis, and
show that Roseland is robust to noise. We also compare
Roseland with other existing algorithms with numerical
simulations.},
Doi = {10.1093/imaiai/iaac013},
Key = {fds370866}
}
@article{fds370867,
Author = {Gavish, M and Su, PC and Talmon, R and Wu, HT},
Title = {Optimal recovery of precision matrix for Mahalanobis
distance from high-dimensional noisy observations in
manifold learning},
Journal = {Information and Inference},
Volume = {11},
Number = {4},
Pages = {1173-1202},
Year = {2022},
Month = {December},
url = {http://dx.doi.org/10.1093/imaiai/iaac010},
Abstract = {Motivated by establishing theoretical foundations for
various manifold learning algorithms, we study the problem
of Mahalanobis distance (MD) and the associated precision
matrix estimation from high-dimensional noisy data. By
relying on recent transformative results in covariance
matrix estimation, we demonstrate the sensitivity of MD and
the associated precision matrix to measurement noise,
determining the exact asymptotic signal-to-noise ratio at
which MD fails, and quantifying its performance otherwise.
In addition, for an appropriate loss function, we propose an
asymptotically optimal shrinker, which is shown to be
beneficial over the classical implementation of the MD, both
analytically and in simulations. The result is extended to
the manifold setup, where the nonlinear interaction between
curvature and high-dimensional noise is taken care of. The
developed solution is applied to study a multi-scale
reduction problem in the dynamical system
analysis.},
Doi = {10.1093/imaiai/iaac010},
Key = {fds370867}
}
@article{fds365617,
Author = {Steinerberger, S and Wu, HT},
Title = {Eigenvector Phase Retrieval: Recovering eigenvectors from
the absolute value of their entries},
Journal = {Linear Algebra and Its Applications},
Volume = {652},
Pages = {239-252},
Year = {2022},
Month = {November},
url = {http://dx.doi.org/10.1016/j.laa.2022.08.002},
Abstract = {We consider the eigenvalue problem Ax=λx where A∈Rn×n
and the eigenvalue is also real λ∈R. If we are given A,
λ and, additionally, the absolute value of the entries of x
(the vector (|xi|)i=1n), is there a fast way to recover x?
In particular, can this be done quicker than computing x
from scratch? This may be understood as a special case of
the phase retrieval problem. We present a randomized
algorithm which provably converges in expectation whenever
λ is a simple eigenvalue. The problem should become easier
when |λ| is large and we discuss another algorithm for that
case as well.},
Doi = {10.1016/j.laa.2022.08.002},
Key = {fds365617}
}
@article{fds359731,
Author = {Huang, WK and Chung, YM and Wang, YB and Mandel, JE and Wu,
HT},
Title = {Airflow recovery from thoracic and abdominal movements using
synchrosqueezing transform and locally stationary Gaussian
process regression},
Journal = {Computational Statistics and Data Analysis},
Volume = {174},
Pages = {107384-107384},
Publisher = {Elsevier BV},
Year = {2022},
Month = {October},
url = {http://dx.doi.org/10.1016/j.csda.2021.107384},
Abstract = {A wealth of information about respiratory system is encoded
in the airflow signal. While direct measurement of airflow
via spirometer with an occlusive seal is the gold standard,
this may not be practical for ambulatory monitoring of
patients. Advances in sensor technology have made
measurement of motion of the thorax and abdomen feasible
with small inexpensive devices, but estimating airflow from
these time series is challenging due to the presence of
complicated nonstationary oscillatory signals. To properly
extract the relevant oscillatory features from thoracic and
abdominal movement, a nonlinear-type time-frequency analysis
tool, the synchrosqueezing transform, is employed; these
features are then used to estimate the airflow by a locally
stationary Gaussian process regression. It is shown that,
using a dataset that contains respiratory signals under
normal sleep conditions, accurate airflow out-of-sample
predictions, and hence the precise estimation of an
important physiological quantity, inspiration respiration
ratio, can be achieved by fitting the proposed model both in
the intra- and inter-subject setups. The method is also
applied to a more challenging case, where subjects under
general anesthesia underwent transitions from pressure
support to unassisted ventilation to further demonstrate the
utility of the proposed method.},
Doi = {10.1016/j.csda.2021.107384},
Key = {fds359731}
}
@article{fds369060,
Author = {Sourisseau, M and Wu, HT and Zhou, Z},
Title = {ASYMPTOTIC ANALYSIS OF SYNCHROSQUEEZING TRANSFORM—TOWARD
STATISTICAL INFERENCE WITH NONLINEAR-TYPE TIME-FREQUENCY
ANALYSIS},
Journal = {Annals of Statistics},
Volume = {50},
Number = {5},
Pages = {2694-2712},
Year = {2022},
Month = {October},
url = {http://dx.doi.org/10.1214/22-AOS2203},
Abstract = {We provide a statistical analysis of a tool in
nonlinear-type time-frequency analysis, the synchrosqueezing
transform (SST), for both the null and nonnull cases. The
intricate nonlinear interaction of different quantities in
SST is quantified by carefully analyzing relevant
multivariate complex Gaussian random variables.
Specifically, we provide the quotient distribution of
dependent and improper complex Gaussian random variables.
Then a central limit theorem result for SST is established.
As an example, we provide a block bootstrap scheme based on
the established SST theory to test if a given time series
contains oscillatory components.},
Doi = {10.1214/22-AOS2203},
Key = {fds369060}
}
@article{fds373902,
Author = {Cheng, X and Wu, H-T},
Title = {Convergence of graph Laplacian with kNN self-tuned
kernels},
Journal = {Information and Inference: A Journal of the
IMA},
Volume = {11},
Number = {3},
Pages = {889-957},
Publisher = {Oxford University Press (OUP)},
Year = {2022},
Month = {September},
url = {http://dx.doi.org/10.1093/imaiai/iaab019},
Abstract = {<jats:title>Abstract</jats:title> <jats:p>Kernelized Gram
matrix $W$ constructed from data points $\{x_i\}_{i=1}^N$ as
$W_{ij}= k_0( \frac{ \| x_i - x_j \|^2} {\sigma ^2} ) $ is
widely used in graph-based geometric data analysis and
unsupervised learning. An important question is how to
choose the kernel bandwidth $\sigma $, and a common practice
called self-tuned kernel adaptively sets a $\sigma _i$ at
each point $x_i$ by the $k$-nearest neighbor (kNN) distance.
When $x_i$s are sampled from a $d$-dimensional manifold
embedded in a possibly high-dimensional space, unlike with
fixed-bandwidth kernels, theoretical results of graph
Laplacian convergence with self-tuned kernels have been
incomplete. This paper proves the convergence of graph
Laplacian operator $L_N$ to manifold (weighted-)Laplacian
for a new family of kNN self-tuned kernels $W^{(\alpha
)}_{ij} = k_0( \frac{ \| x_i - x_j \|^2}{ \epsilon \hat{\rho
}(x_i) \hat{\rho }(x_j)})/\hat{\rho }(x_i)^\alpha \hat{\rho
}(x_j)^\alpha $, where $\hat{\rho }$ is the estimated
bandwidth function by kNN and the limiting operator is also
parametrized by $\alpha $. When $\alpha = 1$, the limiting
operator is the weighted manifold Laplacian $\varDelta _p$.
Specifically, we prove the point-wise convergence of $L_N f
$ and convergence of the graph Dirichlet form with rates.
Our analysis is based on first establishing a $C^0$
consistency for $\hat{\rho }$ which bounds the relative
estimation error $|\hat{\rho } - \bar{\rho }|/\bar{\rho }$
uniformly with high probability, where $\bar{\rho } =
p^{-1/d}$ and $p$ is the data density function. Our
theoretical results reveal the advantage of the self-tuned
kernel over the fixed-bandwidth kernel via smaller variance
error in low-density regions. In the algorithm, no prior
knowledge of $d$ or data density is needed. The theoretical
results are supported by numerical experiments on simulated
data and hand-written digit image data.</jats:p>},
Doi = {10.1093/imaiai/iaab019},
Key = {fds373902}
}
@article{fds367328,
Author = {Alian, A and Lo, YL and Shelley, K and Wu, HT},
Title = {RECONSIDER PHASE RECONSTRUCTION IN SIGNALS WITH DYNAMIC
PERIODICITY FROM THE MODERN SIGNAL PROCESSING
PERSPECTIVE},
Journal = {Foundations of Data Science},
Volume = {4},
Number = {3},
Pages = {355-393},
Year = {2022},
Month = {September},
url = {http://dx.doi.org/10.3934/fods.2022010},
Abstract = {Phase is the most fundamental physical quantity when we
study an oscillatory time series. There have been many tools
aiming to estimate phase, and most of them are developed
based on the analytic function model. Unfortunately, these
analytic function model based tools might be limited in
handling modern signals with intrinsic nonstartionary
structure, for example, biomedical signals composed of
multiple oscillatory components, each with time-varying
frequency, amplitude, and non-sinusoidal oscillation. There
are several consequences of such limitation, and we
specifically focus on the one that phases estimated from
signals simultaneously recorded from different sensors for
the same physiological system from the same subject might be
different. This fact might challenge reproducibility,
communication, and scientific interpretation. Thus, we need
a standardized approach with theoretical support over a
unified model. In this paper, after summarizing existing
models for phase and discussing the main challenge caused by
the above-mentioned intrinsic nonstartionary structure, we
introduce the adaptive non-harmonic model (ANHM), provide a
definition of phase called fundamental phase, which is a
vector-valued function describing the dynamics of all
oscillatory components in the signal, and suggest a
time-varying bandpass filter (tvBPF) scheme based on
time-frequency analysis tools to estimate the fundamental
phase. The proposed approach is validated with a simulated
database and a real-world database with experts’ labels,
and it is applied to two real-world databases, each of which
has biomedical signals recorded from different sensors, to
show how to standardize the definition of phase in the
real-world experimental environment. We report that the
phase describing a physiological system, if properly modeled
and extracted, is immune to the selected sensor for that
system, while other approaches might fail. In conclusion,
the proposed approach resolves the above-mentioned
scientific challenge. We expect its scientific impact on a
broad range of applications.},
Doi = {10.3934/fods.2022010},
Key = {fds367328}
}
@article{fds364104,
Author = {Zimmermann, P and Antonelli, MC and Sharma, R and Müller, A and Zelgert, C and Fabre, B and Wenzel, N and Wu, H-T and Frasch, MG and Lobmaier, SM},
Title = {Prenatal stress perturbs fetal iron homeostasis in a sex
specific manner.},
Journal = {Scientific reports},
Volume = {12},
Number = {1},
Pages = {9341},
Year = {2022},
Month = {June},
url = {http://dx.doi.org/10.1038/s41598-022-13633-z},
Abstract = {The adverse effects of maternal prenatal stress (PS) on
child's neurodevelopment warrant the establishment of
biomarkers that enable early interventional therapeutic
strategies. We performed a prospective matched double cohort
study screening 2000 pregnant women in third trimester with
Cohen Perceived Stress Scale-10 (PSS-10) questionnaire; 164
participants were recruited and classified as stressed and
control group (SG, CG). Fetal cord blood iron parameters of
107 patients were measured at birth. Transabdominal
electrocardiograms-based Fetal Stress Index (FSI) was
derived. We investigated sex contribution to group
differences and conducted causal inference analyses to
assess the total effect of PS exposure on iron homeostasis
using a directed acyclic graph (DAG) approach. Differences
are reported for p < 0.05 unless noted otherwise.
Transferrin saturation was lower in male stressed neonates.
The minimum adjustment set of the DAG to estimate the total
effect of PS exposure on fetal ferritin iron biomarkers
consisted of maternal age and socioeconomic status: SG
revealed a 15% decrease in fetal ferritin compared with CG.
Mean FSI was higher among SG than among CG. FSI-based timely
detection of fetuses affected by PS can support early
individualized iron supplementation and neurodevelopmental
follow-up to prevent long-term sequelae due to
PS-exacerbated impairment of the iron homeostasis.},
Doi = {10.1038/s41598-022-13633-z},
Key = {fds364104}
}
@article{fds364333,
Author = {Wu, HT and Wu, N},
Title = {Strong uniform consistency with rates for kernel density
estimators with general kernels on manifolds},
Journal = {Information and Inference},
Volume = {11},
Number = {2},
Pages = {781-799},
Year = {2022},
Month = {June},
url = {http://dx.doi.org/10.1093/imaiai/iaab014},
Abstract = {When analyzing modern machine learning algorithms, we may
need to handle kernel density estimation (KDE) with
intricate kernels that are not designed by the user and
might even be irregular and asymmetric. To handle this
emerging challenge, we provide a strong uniform consistency
result with the $L^\infty $ convergence rate for KDE on
Riemannian manifolds with Riemann integrable kernels (in the
ambient Euclidean space). We also provide an $L^1$
consistency result for kernel density estimation on
Riemannian manifolds with Lebesgue integrable kernels. The
isotropic kernels considered in this paper are different
from the kernels in the Vapnik-Chervonenkis class that are
frequently considered in statistics society. We illustrate
the difference when we apply them to estimate the
probability density function. Moreover, we elaborate the
delicate difference when the kernel is designed on the
intrinsic manifold and on the ambient Euclidian space, both
might be encountered in practice. At last, we prove the
necessary and sufficient condition for an isotropic kernel
to be Riemann integrable on a submanifold in the Euclidean
space.},
Doi = {10.1093/imaiai/iaab014},
Key = {fds364333}
}
@article{fds361191,
Author = {Dunson, DB and Wu, HT and Wu, N},
Title = {Graph based Gaussian processes on restricted
domains},
Journal = {Journal of the Royal Statistical Society. Series B:
Statistical Methodology},
Volume = {84},
Number = {2},
Pages = {414-439},
Year = {2022},
Month = {April},
url = {http://dx.doi.org/10.1111/rssb.12486},
Abstract = {In nonparametric regression, it is common for the inputs to
fall in a restricted subset of Euclidean space. Typical
kernel-based methods that do not take into account the
intrinsic geometry of the domain across which observations
are collected may produce sub-optimal results. In this
article, we focus on solving this problem in the context of
Gaussian process (GP) models, proposing a new class of Graph
Laplacian based GPs (GL-GPs), which learn a covariance that
respects the geometry of the input domain. As the heat
kernel is intractable computationally, we approximate the
covariance using finitely-many eigenpairs of the Graph
Laplacian (GL). The GL is constructed from a kernel which
depends only on the Euclidean coordinates of the inputs.
Hence, we can benefit from the full knowledge about the
kernel to extend the covariance structure to newly arriving
samples by a Nyström type extension. We provide substantial
theoretical support for the GL-GP methodology, and
illustrate performance gains in various applications.},
Doi = {10.1111/rssb.12486},
Key = {fds361191}
}
@article{fds359843,
Author = {Chiu, NT and Huwiler, S and Ferster, ML and Karlen, W and Wu, HT and Lustenberger, C},
Title = {Get rid of the beat in mobile EEG applications: A framework
towards automated cardiogenic artifact detection and removal
in single-channel EEG},
Journal = {Biomedical Signal Processing and Control},
Volume = {72},
Year = {2022},
Month = {February},
url = {http://dx.doi.org/10.1016/j.bspc.2021.103220},
Abstract = {Brain activity recordings outside clinical or laboratory
settings using mobile EEG systems have gained popular
interest allowing for realistic long-term monitoring and
eventually leading to identification of possible biomarkers
for diseases. The less obtrusive, minimized systems (e.g.,
single-channel EEG, no ECG reference) have the drawback of
artifact contamination with varying intensity that are
particularly difficult to identify and remove. We developed
brMEGA, the first open-source algorithm for automated
detection and removal of cardiogenic artifacts using
non-linear time-frequency analysis and machine learning to
(1) detect whether and where cardiogenic artifacts exist,
and (2) remove those artifacts. We compare our algorithm
against visual artifact identification and a previously
established approach and validate it in one real and
semi-real datasets. We demonstrated that brMEGA successfully
identifies and substantially removes cardiogenic artifacts
in single-channel EEG recordings. Moreover, recovery of
cardiogenic artifacts, if present, gives the opportunity for
future extraction of heart rate features without ECG
measurement.},
Doi = {10.1016/j.bspc.2021.103220},
Key = {fds359843}
}
@article{fds363233,
Author = {Liu, GR and Sheu, YC and Wu, HT},
Title = {Asymptotic Analysis of higher-order scattering transform of
Gaussian processes},
Journal = {Electronic Journal of Probability},
Volume = {27},
Year = {2022},
Month = {January},
url = {http://dx.doi.org/10.1214/22-EJP766},
Abstract = {We analyze the scattering transform with the quadratic
nonlinearity (STQN) of Gaussian processes without depth
limitation. STQN is a nonlinear transform that involves a
sequential interlacing convolution and nonlinear operators,
which is motivated to model the deep convolutional neural
network. We prove that with a proper normalization, the
output of STQN converges to a chi-square process with one
degree of freedom in the finite dimensional distribution
sense, and we provide a total variation distance control of
this convergence at each time that converges to zero at an
exponential rate. To show these, we derive a recursive
formula to represent the intricate nonlinearity of STQN by a
linear combination of Wiener chaos, and then apply the
Malliavin calculus and Stein’s method to achieve the
goal.},
Doi = {10.1214/22-EJP766},
Key = {fds363233}
}
@article{fds363446,
Author = {Shen, C and Lin, YT and Wu, HT},
Title = {Robust and scalable manifold learning via landmark diffusion
for long-term medical signal processing},
Journal = {Journal of Machine Learning Research},
Volume = {23},
Year = {2022},
Month = {January},
Abstract = {Motivated by analyzing long-termphysiological time series,
we design a robust and scalable spectral embedding algorithm
that we refer to as RObust and Scalable Embedding via
LANdmark Diffusion ( Roseland). The key is designing a
diffusion process on the dataset where the diffusion is done
via a small subset called the landmark set. Roseland is
theoretically justified under the manifold model, and its
computational complexity is comparable with commonly applied
subsampling scheme such as the Nyström extension.
Specifically, when there are n data points in Rq and nβ
points in the landmark set, where β ∈ (0; 1), the
computational complexity of Roseland is O(n1+2β + qn1+β),
while that of Nystrom is O(n2:81β +qn1+2β). To demonstrate
the potential of Roseland, we apply it to three datasets and
compare it with several other existing algorithms. First, we
apply Roseland to the task of spectral clustering using the
MNIST dataset (70,000 images), achieving 85% accuracy when
the dataset is clean and 78% accuracy when the dataset is
noisy. Compared with other subsampling schemes, overall
Roseland achieves a better performance. Second, we apply
Roseland to the task of image segmentation using images from
COCO. Finally, we demonstrate how to apply Roseland to
explore long-term arterial blood pressure waveform dynamics
during a liver transplant operation lasting for 12 hours. In
conclusion, Roseland is scalable and robust, and it has a
potential for analyzing large datasets.},
Key = {fds363446}
}
@article{fds370365,
Author = {Baldazzi, G and Pani, D and Wu, HT},
Title = {Extraction Algorithm for Morphologically Preserved
Non-Invasive Multi-Channel Fetal ECG},
Journal = {Computing in Cardiology},
Volume = {2022-September},
Year = {2022},
Month = {January},
ISBN = {9798350300970},
url = {http://dx.doi.org/10.22489/CinC.2022.373},
Abstract = {Non-invasive fetal ECG (fECG) is a promising technique that
could allow low-cost and risk-free diagnosis, and long-term
monitoring of fetal cardiac wellbeing. However, the low
quality of the fECG extracted from non-invasive abdominal
recordings hampers its adoption in clinical practice. In
this work, a new algorithm for the recovery of clean and
morphologically preserved fECG signals from multi-channel
trans-abdominal recordings is presented. The proposed method
exploits optimal shrinkage and nonlocal median algorithms,
along with a de-shape short-time Fourier transform-based
detection, to recover high-quality fECG traces from a
morphological perspective, while ensuring very high
performance also in terms of fetal QRS detection. On a small
dataset, composed of three real 20 min-long four-channel
abdominal ECG recordings, a preliminary performance
assessment of the proposed fECG extraction method in terms
of fetal QRS detection capabilities revealed a median
accuracy of 95.8% and F1 score of 97.9%. The obtained
results suggest the possibility of successfully applying
this approach for an effective non-invasive fECG extraction,
deserving further investigations on larger real and
synthetic datasets.},
Doi = {10.22489/CinC.2022.373},
Key = {fds370365}
}
@article{fds371567,
Author = {Chen, P-L and Chen, Y-C and Tu, P-H and Liu, T-C and Chen, M-C and Wu, H-T and Yeap, M-C and Yeh, C-H and Lu, C-S and Chen, C-C},
Title = {Subthalamic high-beta oscillation informs the outcome of
deep brain stimulation in patients with Parkinson's
disease.},
Journal = {Frontiers in human neuroscience},
Volume = {16},
Pages = {958521},
Year = {2022},
Month = {January},
url = {http://dx.doi.org/10.3389/fnhum.2022.958521},
Abstract = {<h4>Background</h4>The therapeutic effect of deep brain
stimulation (DBS) of the subthalamic nucleus (STN) for
Parkinson's disease (PD) is related to the modulation of
pathological neural activities, particularly the
synchronization in the <i>β</i> band (13-35 Hz). However,
whether the local <i>β</i> activity in the STN region can
directly predict the stimulation outcome remains
unclear.<h4>Objective</h4>We tested the hypothesis that
low-<i>β</i> (13-20 Hz) and/or high-<i>β</i> (20-35 Hz)
band activities recorded from the STN region can predict DBS
efficacy.<h4>Methods</h4>Local field potentials (LFPs) were
recorded in 26 patients undergoing deep brain stimulation
surgery in the subthalamic nucleus area. Recordings were
made after the implantation of the DBS electrode prior to
its connection to a stimulator. The maximum normalized
powers in the theta (4-7 Hz), alpha (7-13 Hz), low-<i>β</i>
(13-20 Hz), high-<i>β</i> (20-35 Hz), and low-γ (40-55 Hz)
subbands in the postoperatively recorded LFP were correlated
with the stimulation-induced improvement in contralateral
tremor or bradykinesia-rigidity. The distance between the
contact selected for stimulation and the contact with the
maximum subband power was correlated with the stimulation
efficacy. Following the identification of the potential
predictors by the significant correlations, a multiple
regression analysis was performed to evaluate their effect
on the outcome.<h4>Results</h4>The maximum high-<i>β</i>
power was positively correlated with bradykinesia-rigidity
improvement (<i>r</i> <sub><i>s</i></sub> = 0.549, <i>p</i>
< 0.0001). The distance to the contact with maximum
high-<i>β</i> power was negatively correlated with
bradykinesia-rigidity improvement (<i>r</i>
<sub><i>s</i></sub> = -0.452, <i>p</i> < 0.001). No
significant correlation was observed with low-<i>β</i>
power. The maximum high-<i>β</i> power and the distance to
the contact with maximum high-<i>β</i> power were both
significant predictors for bradykinesia-rigidity improvement
in the multiple regression analysis, explaining 37.4% of the
variance altogether. Tremor improvement was not
significantly correlated with any frequency.<h4>Conclusion</h4>High-<i>β</i>
oscillations, but not low-<i>β</i> oscillations, recorded
from the STN region with the DBS lead can inform
stimulation-induced improvement in contralateral
bradykinesia-rigidity in patients with PD. High-<i>β</i>
oscillations can help refine electrode targeting and inform
contact selection for DBS therapy.},
Doi = {10.3389/fnhum.2022.958521},
Key = {fds371567}
}
@article{fds371628,
Author = {Chew, J and Steach, H and Viswanath, S and Wu, HT and Hirn, M and Needell,
D and Vesely, MD and Krishnaswamy, S and Perlmutter,
M},
Title = {THE MANIFOLD SCATTERING TRANSFORM FOR HIGH-DIMENSIONAL POINT
CLOUD DATA},
Journal = {Proceedings of Machine Learning Research},
Volume = {196},
Pages = {67-78},
Year = {2022},
Month = {January},
Abstract = {The manifold scattering transform is a deep feature
extractor for data defined on a Riemannian manifold. It is
one of the first examples of extending convolutional neural
network-like operators to general manifolds. The initial
work on this model focused primarily on its theoretical
stability and invariance properties but did not provide
methods for its numerical implementation except in the case
of two-dimensional surfaces with predefined meshes. In this
work, we present practical schemes, based on the theory of
diffusion maps, for implementing the manifold scattering
transform to datasets arising in naturalistic systems, such
as single cell genetics, where the data is a
high-dimensional point cloud modeled as lying on a
low-dimensional manifold. We show that our methods are
effective for signal classification and manifold
classification tasks.},
Key = {fds371628}
}
@article{fds375332,
Author = {Chen, Z and Wu, HT},
Title = {WHEN RAMANUJAN MEETS TIME-FREQUENCY ANALYSIS IN COMPLICATED
TIME SERIES ANALYSIS},
Journal = {Pure and Applied Analysis},
Volume = {4},
Number = {4},
Pages = {629-673},
Year = {2022},
Month = {January},
url = {http://dx.doi.org/10.2140/paa.2022.4.629},
Abstract = {To handle time series with complicated oscillatory
structure, we propose a novel time-frequency (TF) analysis
tool that fuses the short-time Fourier transform (STFT) and
periodic transform (PT). As many time series oscillate with
time-varying frequency, amplitude and nonsinusoidal
oscillatory pattern, a direct application of PT or STFT
might not be suitable. However, we show that by combining
them in a proper way, we obtain a powerful TF analysis tool.
We first combine the Ramanujan sums and l1 penalization to
implement the PT. We call the algorithm Ramanujan PT (RPT).
The RPT is of its own interest for other applications, like
analyzing short signals composed of components with integer
periods, but that is not the focus of this paper. Second,
the RPT is applied to modify the STFT and generate a novel
TF representation of the complicated time series that
faithfully reflects the instantaneous frequency information
of each oscillatory component. We coin the proposed TF
analysis the Ramanujan de-shape (RDS) and vectorized RDS
(vRDS). In addition to showing some preliminary analysis
results on complicated biomedical signals, we provide
theoretical analysis about the RPT. Specifically, we show
that the RPT is robust to three commonly encountered noises,
including envelop fluctuation, jitter and additive
noise.},
Doi = {10.2140/paa.2022.4.629},
Key = {fds375332}
}
@article{fds361464,
Author = {Chen, Z and Wu, H-T},
Title = {Disentangling modes with crossover instantaneous frequencies
by synchrosqueezed chirplet transforms, from theory to
application},
Year = {2021},
Month = {December},
Abstract = {Analysis of signals with oscillatory modes with crossover
instantaneous frequencies is a challenging problem in time
series analysis. One way to handle this problem is lifting
the 2-dimensional time-frequency representation to a
3-dimensional representation, called time-frequency-chirp
rate (TFC) representation, by adding one extra chirp rate
parameter so that crossover frequencies are disentangled in
higher dimension. The chirplet transform is an algorithm for
this lifting idea, which leads to a TFC representation.
However, in practice, we found that it has a strong
``blurring'' effect in the chirp rate axis, which limits its
application in real-world data. Moreover, to our knowledge,
we have limited mathematical understanding of the chirplet
transform in the literature. Motivated by the need for the
real-world data analysis, in this paper, we propose the
synchrosqueezed chirplet transform (SCT) that enhances the
TFC representation given by the chirplet transform. The
resulting concentrated TFC representation has high contrast
so that one can better distinguish different modes with
crossover instantaneous frequencies. The basic idea is to
use the phase information in the chirplet transform to
determine a reassignment rule that sharpens the TFC
representation determined by the chirplet transform. We also
analyze the chirplet transform and provide theoretical
guarantees of SCT.},
Key = {fds361464}
}
@article{fds361465,
Author = {Steinerberger, S and Wu, H-T},
Title = {Fundamental component enhancement via adaptive nonlinear
activation functions},
Year = {2021},
Month = {December},
Abstract = {In many real world oscillatory signals, the fundamental
component of a signal $f(t)$ might be weak or does not
exist. This makes it difficult to estimate the instantaneous
frequency of the signal. Traditionally, researchers apply
the rectification trick, working with $|f(t)|$ or
$\mbox{ReLu}(f(t))$ instead, to enhance the fundamental
component. This raises an interesting question: what type of
nonlinear function $g:\mathbb{R} \rightarrow \mathbb{R}$ has
the property that $g(f(t))$ has a more pronounced
fundamental frequency? $g(t) = |t|$ and $g(t) =
\mbox{ReLu}(t)$ seem to work well in practice; we propose a
variant of $g(t) = 1/(1-|t|)$ and provide a theoretical
guarantee. Several simulated signals and real signals are
analyzed to demonstrate the performance of the proposed
solution.},
Key = {fds361465}
}
@article{fds362343,
Author = {Hamilton, W and Marzuola, JL and Wu, HT},
Title = {On the behavior of 1-Laplacian ratio cuts on nearly
rectangular domains},
Journal = {Information and Inference},
Volume = {10},
Number = {4},
Pages = {1563-1610},
Publisher = {Oxford University Press (OUP)},
Year = {2021},
Month = {December},
url = {http://dx.doi.org/10.1093/imaiai/iaaa034},
Abstract = {The p-Laplacian has attracted more and more attention in
data analysis disciplines in the past decade. However, there
is still a knowledge gap about its behavior, which limits
its practical application. In this paper, we are interested
in its iterative behavior in domains contained in
two-dimensional Euclidean space. Given a connected set Ω0
⊂ R2, define a sequence of sets (Ωn)∞n=0 where Ωn+1 is
the subset of Ωn where the first eigenfunction of the
(properly normalized) Neumann p-Laplacian −Δ(p)φ =
λ1|φ|p−2φ is positive (or negative). For p = 1, this is
also referred to as the ratio cut of the domain. We
conjecture that these sets converge to the set of rectangles
with eccentricity bounded by 2 in the Gromov–Hausdorff
distance as long as they have a certain distance to the
boundary ∂Ω0. We establish some aspects of this
conjecture for p = 1 where we prove that (1) the 1-Laplacian
spectral cut of domains sufficiently close to rectangles is
a circular arc that is closer to flat than the original
domain (leading eventually to quadrilaterals) and (2)
quadrilaterals close to a rectangle of aspect ratio 2 stay
close to quadrilaterals and move closer to rectangles in a
suitable metric. We also discuss some numerical aspects and
pose many open questions.},
Doi = {10.1093/imaiai/iaaa034},
Key = {fds362343}
}
@article{fds361466,
Author = {Ding, X and Wu, H-T},
Title = {How do kernel-based sensor fusion algorithms behave under
high dimensional noise?},
Year = {2021},
Month = {November},
Abstract = {We study the behavior of two kernel based sensor fusion
algorithms, nonparametric canonical correlation analysis
(NCCA) and alternating diffusion (AD), under the nonnull
setting that the clean datasets collected from two sensors
are modeled by a common low dimensional manifold embedded in
a high dimensional Euclidean space and the datasets are
corrupted by high dimensional noise. We establish the
asymptotic limits and convergence rates for the eigenvalues
of the associated kernel matrices assuming that the sample
dimension and sample size are comparably large, where NCCA
and AD are conducted using the Gaussian kernel. It turns out
that both the asymptotic limits and convergence rates depend
on the signal-to-noise ratio (SNR) of each sensor and
selected bandwidths. On one hand, we show that if NCCA and
AD are directly applied to the noisy point clouds without
any sanity check, it may generate artificial information
that misleads scientists' interpretation. On the other hand,
we prove that if the bandwidths are selected adequately,
both NCCA and AD can be made robust to high dimensional
noise when the SNRs are relatively large.},
Key = {fds361466}
}
@article{fds360071,
Author = {Chen, HY and Malik, J and Wu, HT and Wang, CL},
Title = {Is the median hourly ambulatory heart rate range helpful in
stratifying mortality risk among newly diagnosed atrial
fibrillation patients?},
Journal = {Journal of Personalized Medicine},
Volume = {11},
Number = {11},
Year = {2021},
Month = {November},
url = {http://dx.doi.org/10.3390/jpm11111202},
Abstract = {Background: The application of heart rate variability is
problematic in patients with atrial fibrillation (AF). This
study aims to explore the associations between all-cause
mortality and the median hourly ambulatory heart rate range
(ÃHRR24hr) compared with other parameters obtained from the
Holter monitor in patients with newly diagnosed AF. Material
and Methods: A total of 30 parameters obtained from 521
persistent AF patients’ Holter monitor were analyzed
retrospectively from 1 January 2010 to 31 July 2014. Every
patient was followed up to the occurrence of death or the
end of 30 June 2017. Results: ÃHRR24hr was the most
feasible Holter parameter. Lower ÃHRR24hr was associated
with increased risk of all-cause mortality (adjusted hazard
ratio [aHR] for every 10-bpm reduction: 2.70, 95% confidence
interval [CI]: 1.75–4.17, p < 0.001). The C-statistic of
ÃHRR24hr alone was 0.707 (95% CI: 0.658–0.756), and 0.697
(95% CI: 0.650–0.744) for the CHA2DS2-VASc score alone. By
combining ÃHRR24hr with the CHA2DS2-VASc score, the
C-statistic could improve to 0.764 (95% CI: 0.722–0.806).
While using 20 bpm as the cut-off value, the aHR was 3.66
(95% CI: 2.05–6.52) for patients with ÃHRR24hr < 20 bpm
in contrast to patients with ÃHRR24hr ≥ 20 bpm.
Conclusions: ÃHRR24hr could be helpful for risk
stratification for AF in addition to the CHA2DS2-VASc
score.},
Doi = {10.3390/jpm11111202},
Key = {fds360071}
}
@article{fds357498,
Author = {Dunson, DB and Wu, HT and Wu, N},
Title = {Spectral convergence of graph Laplacian and heat kernel
reconstruction in L∞ from random
samples},
Journal = {Applied and Computational Harmonic Analysis},
Volume = {55},
Pages = {282-336},
Year = {2021},
Month = {November},
url = {http://dx.doi.org/10.1016/j.acha.2021.06.002},
Abstract = {In the manifold setting, we provide a series of spectral
convergence results quantifying how the eigenvectors and
eigenvalues of the graph Laplacian converge to the
eigenfunctions and eigenvalues of the Laplace-Beltrami
operator in the L∞ sense. Based on these results,
convergence of the proposed heat kernel approximation
algorithm, as well as the convergence rate, to the exact
heat kernel is guaranteed. To our knowledge, this is the
first work exploring the spectral convergence in the L∞
sense and providing a numerical heat kernel reconstruction
from the point cloud with theoretical guarantees.},
Doi = {10.1016/j.acha.2021.06.002},
Key = {fds357498}
}
@article{fds355551,
Author = {Sourisseau, M and Wang, YG and Womersley, RS and Wu, HT and Yu,
WH},
Title = {Improve concentration of frequency and time (ConceFT) by
novel complex spherical designs},
Journal = {Applied and Computational Harmonic Analysis},
Volume = {54},
Pages = {137-144},
Publisher = {Elsevier BV},
Year = {2021},
Month = {September},
url = {http://dx.doi.org/10.1016/j.acha.2021.02.003},
Abstract = {Concentration of frequency and time (ConceFT) is a
generalized multitaper algorithm introduced to analyze
complicated non-stationary time series. To avoid the
randomness in the original ConceFT algorithm, we apply the
novel complex spherical design technique to standardize
ConceFT, which we coin CQU-ConceFT. The proposed CQU-ConceFT
is applied to visualize the spindle structure in the
electroencephalogram signal during the N2 sleep stage and
other physiological time series.},
Doi = {10.1016/j.acha.2021.02.003},
Key = {fds355551}
}
@article{fds355552,
Author = {Tan, C and Zhang, L and Wu, HT and Qian, T},
Title = {A novel feature representation approach for single-lead
heartbeat classification based on adaptive Fourier
decomposition},
Journal = {International Journal of Wavelets, Multiresolution and
Information Processing},
Volume = {19},
Number = {5},
Year = {2021},
Month = {September},
url = {http://dx.doi.org/10.1142/S0219691321500107},
Abstract = {This paper proposes a novel feature representation approach
for heartbeat classification using single-lead
electrocardiogram (ECG) signals based on adaptive Fourier
decomposition (AFD). AFD is a recently developed signal
processing tool that provides useful morphological features,
which are referred as AFD-derived instantaneous frequency
(IF) features and differ from those provided by traditional
tools. The AFD-derived IF features, together with ECG
landmark features and RR interval features, are trained by a
support vector machine to perform the classification. The
proposed method improves the average accuracy of the feature
extraction-based methods, reaching a level comparable to
deep learning but with less training data, and at the same
time being interpretable for the learned features. It also
greatly reduces the dimension of the feature set, which is a
disadvantage of the feature extraction-based methods,
especially for ECG signals. To evaluate the performance, the
Association for the Advancement of Medical Instrumentation
standard is applied to publicly available benchmark
databases, including the MIT-BIH arrhythmia and MIT-BIH
supraventricular arrhythmia databases, to classify
heartbeats from the single-lead ECG. The overall performance
is compared to selected state-of-the-art automatic heartbeat
classification algorithms, including one-lead and even
several two-lead-based methods. The proposed approach
achieves superior balanced performance and real-time
implementation.},
Doi = {10.1142/S0219691321500107},
Key = {fds355552}
}
@article{fds358344,
Author = {Wu, HT and Lai, TL and Haddad, GG and Muotri, A},
Title = {Oscillatory Biomedical Signals: Frontiers in Mathematical
Models and Statistical Analysis},
Journal = {Frontiers in Applied Mathematics and Statistics},
Volume = {7},
Year = {2021},
Month = {July},
url = {http://dx.doi.org/10.3389/fams.2021.689991},
Abstract = {Herein we describe new frontiers in mathematical modeling
and statistical analysis of oscillatory biomedical signals,
motivated by our recent studies of network formation in the
human brain during the early stages of life and studies
forty years ago on cardiorespiratory patterns during sleep
in infants and animal models. The frontiers involve new
nonlinear-type time–frequency analysis of signals with
multiple oscillatory components, and efficient particle
filters for joint state and parameter estimators together
with uncertainty quantification in hidden Markov models and
empirical Bayes inference.},
Doi = {10.3389/fams.2021.689991},
Key = {fds358344}
}
@article{fds355817,
Author = {DiPietro, JA and Raghunathan, RS and Wu, H-T and Bai, J and Watson, H and Sgambati, FP and Henderson, JL and Pien, GW},
Title = {Fetal heart rate during maternal sleep.},
Journal = {Developmental psychobiology},
Volume = {63},
Number = {5},
Pages = {945-959},
Year = {2021},
Month = {July},
url = {http://dx.doi.org/10.1002/dev.22118},
Abstract = {Despite prolonged and cumulative exposure during gestation,
little is known about the fetal response to maternal sleep.
Eighty-four pregnant women with obesity (based on
pre-pregnancy BMI) participated in laboratory-based
polysomnography (PSG) with continuous fetal
electrocardiogram monitoring at 36 weeks gestation.
Multilevel modeling revealed both correspondence and lack of
it in maternal and fetal heart rate patterns. Fetal heart
rate (fHR) and variability (fHRV), and maternal heart rate
(mHR) and variability (mHRV), all declined during the night,
with steeper rates of decline prior to 01:00. fHR declined
upon maternal sleep onset but was not otherwise associated
with maternal sleep stage; fHRV differed during maternal REM
and NREM. There was frequent maternal waking after sleep
onset (WASO) and fHRV and mHRV were elevated during these
episodes. Cross-correlation analyses revealed little
temporal coupling between maternal and fetal heart rate,
except during WASO, suggesting that any observed
associations in maternal and fetal heart rates during sleep
are the result of other physiological processes.
Implications of the maternal sleep context for the
developing fetus are discussed, including the potential
consequences of the typical sleep fragmentation that
accompanies pregnancy.},
Doi = {10.1002/dev.22118},
Key = {fds355817}
}
@article{fds357478,
Author = {Steinerberger, S and Wu, HT},
Title = {On Zeroes of Random Polynomials and an Application to
Unwinding},
Journal = {International Mathematics Research Notices},
Volume = {2021},
Number = {13},
Pages = {10100-10117},
Publisher = {Oxford University Press (OUP)},
Year = {2021},
Month = {July},
url = {http://dx.doi.org/10.1093/imrn/rnz096},
Abstract = {Let μ be a probability measure in C with a continuous and
compactly supported density function, let z1, zn be
independent random variables, zi ∼ μ, and consider the
random polynomial pn(z) = ∏ k=1n(z-zk).We determine the
asymptotic distribution of left z C: pn(z) = pn(0). In
particular, if mu is radial around the origin, then those
solutions are also distributed according to mu as n.
Generally, the distribution of the solutions will reproduce
parts of mu and condense another part on curves. We use
these insights to study the behavior of the Blaschke
unwinding series on random data.},
Doi = {10.1093/imrn/rnz096},
Key = {fds357478}
}
@article{fds367329,
Author = {Lin, YT and Malik, J and Wu, HT},
Title = {WAVE-SHAPE OSCILLATORY MODEL FOR NONSTATIONARY PERIODIC TIME
SERIES ANALYSIS},
Journal = {Foundations of Data Science},
Volume = {3},
Number = {2},
Pages = {99-131},
Year = {2021},
Month = {June},
url = {http://dx.doi.org/10.3934/fods.2021009},
Abstract = {The oscillations observed in many time series, particularly
in biomedicine, exhibit morphological variations over time.
These morphological variations are caused by intrinsic or
extrinsic changes to the state of the generating system,
henceforth referred to as dynamics. To model these time
series (including and specifically pathophysiological ones)
and estimate the underlying dynamics, we provide a novel
wave-shape oscillatory model. In this model, time-dependent
variations in cycle shape occur along a manifold called the
wave-shape manifold. To estimate the wave-shape manifold
associated with an oscillatory time series, study the
dynamics, and visualize the time-dependent changes along the
wave-shape manifold, we propose a novel algorithm coined
Dynamic Diffusion map (DDmap) by applying the
well-established diffusion maps (DM) algorithm to the set of
all observed oscillations. We provide a theoretical
guarantee on the dynamical information recovered by the
DDmap algorithm under the proposed model. Applying the
proposed model and algorithm to arterial blood pressure
(ABP) signals recorded during general anesthesia leads to
the extraction of nociception information. Applying the
wave-shape oscillatory model and the DDmap algorithm to
cardiac cycles in the electrocardiogram (ECG) leads to
ectopy detection and a new ECG-derived respiratory signal,
even when the subject has atrial fibrillation.},
Doi = {10.3934/fods.2021009},
Key = {fds367329}
}
@article{fds350212,
Author = {Wu, H-T and Alian, A and Shelley, K},
Title = {A new approach to complicated and noisy physiological
waveforms analysis: peripheral venous pressure waveform as
an example.},
Journal = {Journal of clinical monitoring and computing},
Volume = {35},
Number = {3},
Pages = {637-653},
Year = {2021},
Month = {May},
url = {http://dx.doi.org/10.1007/s10877-020-00524-9},
Abstract = {We introduce a recently developed nonlinear-type
time-frequency analysis tool, synchrosqueezing transform
(SST), to quantify complicated and noisy physiological
waveform that has time-varying amplitude and frequency. We
apply it to analyze a peripheral venous pressure (PVP)
signal recorded during a seven hours aortic valve
replacement procedure. In addition to showing the captured
dynamics, we also quantify how accurately we can estimate
the instantaneous heart rate from the PVP
signal.},
Doi = {10.1007/s10877-020-00524-9},
Key = {fds350212}
}
@article{fds355949,
Author = {Liu, T-C and Liu, Y-W and Wu, H-T},
Title = {Denoising click-evoked otoacoustic emission signals by
optimal shrinkage.},
Journal = {The Journal of the Acoustical Society of
America},
Volume = {149},
Number = {4},
Pages = {2659},
Publisher = {Acoustical Society of America (ASA)},
Year = {2021},
Month = {April},
url = {http://dx.doi.org/10.1121/10.0004264},
Abstract = {Click-evoked otoacoustic emissions (CEOAEs) are clinically
used as an objective way to infer whether cochlear functions
are normal. However, because the sound pressure level of
CEOAEs is typically much lower than the background noise, it
usually takes hundreds, if not thousands, of repetitions to
estimate the signal with sufficient accuracy. In this paper,
we propose to improve the signal-to-noise ratio (SNR) of
CEOAE signals within limited measurement time by optimal
shrinkage (OS) in two different settings: covariance-based
optimal shrinkage (cOS) and singular value
decomposition-based optimal shrinkage (sOS). By simulation,
the cOS consistently enhanced the SNR by 1-2 dB from a
baseline method that is based on calculating the median. In
real data, however, the cOS cannot enhance the SNR over
1 dB. The sOS achieved a SNR enhancement of 2-3 dB in
simulation and demonstrated capability to enhance the SNR in
real recordings. In addition, the level of enhancement
increases as the baseline SNR decreases. An appealing
property of OS is that it produces an estimate of all single
trials. This property makes it possible to investigate CEOAE
dynamics across a longer period of time when the cochlear
conditions are not strictly stationary.},
Doi = {10.1121/10.0004264},
Key = {fds355949}
}
@article{fds366030,
Author = {Chiu, N-T and Huwiler, S and Ferster, ML and Karlen, W and Wu, H-T and Lustenberger, C},
Title = {Get rid of the beat in mobile EEG applications: A framework
towards automated cardiogenic artifact detection and removal
in single-channel EEG},
Year = {2021},
Month = {February},
url = {http://dx.doi.org/10.1101/2021.02.09.430184},
Abstract = {<jats:title>Abstract</jats:title><jats:p>Brain activity
recordings outside clinical or laboratory settings using
mobile EEG systems have recently gained popular interest
allowing for realistic long-term monitoring and eventually
leading to identification of possible biomarkers for
diseases. The less obtrusive, minimized systems (e.g.
single-channel EEG, no ECG reference) have the drawback of
artifact contamination with varying intensity that are
particularly difficult to identify and remove. We developed
brMEGA, the first algorithm for automated detection and
removal of cardiogenic artifacts using non-linear
time-frequency analysis and machine learning to (1) detect
whether and where cardiogenic artifacts exist, and (2)
remove those artifacts. We compare our algorithm against
visual artifact identification and a previously established
approach and validate it in one real and semi-real datasets.
We demonstrated that brMEGA successfully identifies and
substantially removes cardiogenic artifacts in
single-channel EEG recordings. Moreover, recovery of
cardiogenic artifacts gives the opportunity for future
extraction of heart rate features without ECG
measurement.</jats:p>},
Doi = {10.1101/2021.02.09.430184},
Key = {fds366030}
}
@article{fds352488,
Author = {Liu, G-R and Lin, T-Y and Wu, H-T and Sheu, Y-C and Liu, C-L and Liu, W-T and Yang, M-C and Ni, Y-L and Chou, K-T and Chen, C-H and Wu, D and Lan, C-C and Chiu, K-L and Chiu, H-Y and Lo, Y-L},
Title = {Large-scale assessment of consistency in sleep stage scoring
rules among multiple sleep centers using an interpretable
machine learning algorithm.},
Journal = {Journal of clinical sleep medicine : JCSM : official
publication of the American Academy of Sleep
Medicine},
Volume = {17},
Number = {2},
Pages = {159-166},
Year = {2021},
Month = {February},
url = {http://dx.doi.org/10.5664/jcsm.8820},
Abstract = {<h4>Study objectives</h4>Polysomnography is the gold
standard in identifying sleep stages; however, there are
discrepancies in how technicians use the standards. Because
organizing meetings to evaluate this discrepancy and/or
reach a consensus among multiple sleep centers is
time-consuming, we developed an artificial intelligence
system to efficiently evaluate the reliability and
consistency of sleep scoring and hence the sleep center
quality.<h4>Methods</h4>An interpretable machine learning
algorithm was used to evaluate the interrater reliability
(IRR) of sleep stage annotation among sleep centers. The
artificial intelligence system was trained to learn raters
from 1 hospital and was applied to patients from the same or
other hospitals. The results were compared with the experts'
annotation to determine IRR. Intracenter and intercenter
assessments were conducted on 679 patients without sleep
apnea from 6 sleep centers in Taiwan. Centers with potential
quality issues were identified by the estimated
IRR.<h4>Results</h4>In the intracenter assessment, the
median accuracy ranged from 80.3%-83.3%, with the exception
of 1 hospital, which had an accuracy of 72.3%. In the
intercenter assessment, the median accuracy ranged from
75.7%-83.3% when the 1 hospital was excluded from testing
and training. The performance of the proposed method was
higher for the N2, awake, and REM sleep stages than for the
N1 and N3 stages. The significant IRR discrepancy of the 1
hospital suggested a quality issue. This quality issue was
confirmed by the physicians in charge of the 1
hospital.<h4>Conclusions</h4>The proposed artificial
intelligence system proved effective in assessing IRR and
hence the sleep center quality.},
Doi = {10.5664/jcsm.8820},
Key = {fds352488}
}
@article{fds359230,
Author = {Colominas, MA and Wu, HT},
Title = {Decomposing non-stationary signals with time-varying
wave-shape functions},
Journal = {IEEE Transactions on Signal Processing},
Volume = {69},
Pages = {5094-5104},
Year = {2021},
Month = {January},
url = {http://dx.doi.org/10.1109/TSP.2021.3108678},
Abstract = {Modern time series are usually composed of multiple
oscillatory components, with time-varying frequency and
amplitude contaminated by noise. The signal processing
mission is further challenged if each component has an
oscillatory pattern, or the wave-shape function, far from a
sinusoidal function, and the oscillatory pattern is even
changing from time to time. In practice, if multiple
components exist, it is desirable to robustly decompose the
signal into each component for various purposes, and extract
desired dynamics information. Such challenges have raised a
significant amount of interest in the past decade, but a
satisfactory solution is still lacking. We propose a novel
nonlinear regression scheme to robustly decompose a signal
into its constituting multiple oscillatory components with
time-varying frequency, amplitude and wave-shape function.
We coined the algorithm shape-adaptive mode decomposition
(SAMD). In addition to simulated signals, we apply SAMD to
two physiological signals, impedance pneumography and
electroencephalography. Comparison with existing solutions,
including linear regression, recursive diffeomorphism-based
regression and multiresolution mode decomposition, shows
that our proposal can provide an accurate and meaningful
decomposition with computational efficiency.},
Doi = {10.1109/TSP.2021.3108678},
Key = {fds359230}
}
@article{fds355605,
Author = {Chung, Y-M and Hu, C-S and Lo, Y-L and Wu, H-T},
Title = {A Persistent Homology Approach to Heart Rate Variability
Analysis With an Application to Sleep-Wake
Classification.},
Journal = {Frontiers in physiology},
Volume = {12},
Pages = {637684},
Year = {2021},
Month = {January},
url = {http://dx.doi.org/10.3389/fphys.2021.637684},
Abstract = {Persistent homology is a recently developed theory in the
field of algebraic topology to study shapes of datasets. It
is an effective data analysis tool that is robust to noise
and has been widely applied. We demonstrate a general
pipeline to apply persistent homology to study time series,
particularly the instantaneous heart rate time series for
the heart rate variability (HRV) analysis. The first step is
capturing the shapes of time series from two different
aspects-the persistent homologies and hence persistence
diagrams of its sub-level set and Taken's lag map. Second,
we propose a systematic and computationally efficient
approach to summarize persistence diagrams, which we coined
<i>persistence statistics</i>. To demonstrate our proposed
method, we apply these tools to the HRV analysis and the
sleep-wake, REM-NREM (rapid eyeball movement and non rapid
eyeball movement) and sleep-REM-NREM classification
problems. The proposed algorithm is evaluated on three
different datasets via the cross-database validation scheme.
The performance of our approach is better than the
state-of-the-art algorithms, and the result is consistent
throughout different datasets.},
Doi = {10.3389/fphys.2021.637684},
Key = {fds355605}
}
@article{fds349995,
Author = {Frasch, MG and Shen, C and Wu, H-T and Mueller, A and Neuhaus, E and Bernier, RA and Kamara, D and Beauchaine, TP},
Title = {Brief Report: Can a Composite Heart Rate Variability
Biomarker Shed New Insights About Autism Spectrum Disorder
in School-Aged Children?},
Journal = {Journal of autism and developmental disorders},
Volume = {51},
Number = {1},
Pages = {346-356},
Year = {2021},
Month = {January},
url = {http://dx.doi.org/10.1007/s10803-020-04467-7},
Abstract = {Several studies show altered heart rate variability (HRV) in
autism spectrum disorder (ASD), but findings are neither
universal nor specific to ASD. We apply a set of linear and
nonlinear HRV measures-including phase rectified signal
averaging-to segments of resting ECG data collected from
school-age children with ASD, age-matched typically
developing controls, and children with other psychiatric
conditions characterized by altered HRV (conduct disorder,
depression). We use machine learning to identify time,
frequency, and geometric signal-analytical domains that are
specific to ASD (receiver operating curve area = 0.89).
This is the first study to differentiate children with ASD
from other disorders characterized by altered HRV. Despite a
small cohort and lack of external validation, results
warrant larger prospective studies.},
Doi = {10.1007/s10803-020-04467-7},
Key = {fds349995}
}
@article{fds353809,
Author = {Wang, H-HS and Cahill, D and Panagides, J and Nelson, CP and Wu, H-T and Estrada, C},
Title = {Pattern recognition algorithm to identify detrusor
overactivity on urodynamics.},
Journal = {Neurourology and urodynamics},
Volume = {40},
Number = {1},
Pages = {428-434},
Year = {2021},
Month = {January},
url = {http://dx.doi.org/10.1002/nau.24578},
Abstract = {<h4>Aims</h4>Detrusor overactivity (DO) of the bladder is a
finding on urodynamic studies (UDS) that often correlates
with lower urinary tract symptoms and drives management.
However, UDS interpretation remains nonstandardized. We
sought to develop a mathematical model to reliably identify
DO in UDS.<h4>Methods</h4>We utilized UDS archive files for
studies performed at our institution between 2013 and 2019.
Raw tracings of vesical pressure, abdominal pressure,
detrusor pressure, infused volume, and all annotations
during UDS were obtained. Patients less than 1 year old,
studies with calibration issues, or those with significant
artifacts were excluded. In the training set, five
representative DO patterns were identified. Candidate Pdet
signal segments were matched to representative DO patterns.
Manifold learning and dynamic time warping algorithms were
used. Five-fold cross validation (CV) was used to evaluate
the performance.<h4>Results</h4>A total of 799 UDS studies
were included. The median age was 9 years (range, 1-33).
There were 1,742 DO events that did not overlap with
annotated artifacts (cough, cry, valsalva, movements). The
AUC of the training sets from the five-fold CV was
0.84 ± 0.01. The five-fold CV leads to an overall
accuracy 81.35%, and sensitivity and specificity of
detecting DO events are 76.92% and 81.41%, respectively, in
the testing set.<h4>Conclusions</h4>Our predictive model
using machine learning algorithms provides promising
performance to facilitate automated identification of DO in
UDS. This would allow for standardization and potentially
more reliable UDS interpretation. Signal processing and
machine learning interpretation of the other components of
UDS are forthcoming.},
Doi = {10.1002/nau.24578},
Key = {fds353809}
}
@article{fds354271,
Author = {Ding, X and Wu, HT},
Title = {On the Spectral Property of Kernel-Based Sensor Fusion
Algorithms of High Dimensional Data},
Journal = {IEEE Transactions on Information Theory},
Volume = {67},
Number = {1},
Pages = {640-670},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2021},
Month = {January},
url = {http://dx.doi.org/10.1109/TIT.2020.3026255},
Abstract = {We apply local laws of random matrices and free probability
theory to study the spectral properties of two kernel-based
sensor fusion algorithms, nonparametric canonical
correlation analysis (NCCA) and alternating diffusion (AD),
for two simultaneously recorded high dimensional datasets
under the null hypothesis. The matrix of interest is the
product of the kernel matrices associated with the
databsets, which may not be diagonalizable in general. We
prove that in the regime where dimensions of both random
vectors are comparable to the sample size, if NCCA and AD
are conducted using a smooth kernel function, then the first
few nontrivial eigenvalues will converge to real
deterministic values provided the datasets are independent
Gaussian random vectors. Toward the claimed result, we also
provide a convergence rate of eigenvalues of a kernel
affinity matrix.},
Doi = {10.1109/TIT.2020.3026255},
Key = {fds354271}
}
@article{fds355480,
Author = {Meynard, A and Wu, HT},
Title = {An Efficient Forecasting Approach to Reduce Boundary Effects
in Real-Time Time-Frequency Analysis},
Journal = {IEEE Transactions on Signal Processing},
Volume = {69},
Pages = {1653-1663},
Year = {2021},
Month = {January},
url = {http://dx.doi.org/10.1109/TSP.2021.3062181},
Abstract = {Time-frequency (TF) representations of time series are
intrinsically subject to the boundary effects. As a result,
the structures of signals that are highlighted by the
representations are garbled when approaching the boundaries
of the TF domain. In this paper, for the purpose of
real-time TF information acquisition of nonstationary
oscillatory time series, we propose a numerically efficient
approach for the reduction of such boundary effects. The
solution relies on an extension of the analyzed signal
obtained by a forecasting technique. In the case of the
study of a class of locally oscillating signals, we provide
a theoretical guarantee of the performance of our approach.
Following a numerical verification of the algorithmic
performance of our approach, we validate it by implementing
it on biomedical signals.},
Doi = {10.1109/TSP.2021.3062181},
Key = {fds355480}
}
@article{fds354951,
Author = {Huang, Y-C and Lin, T-Y and Wu, H-T and Chang, P-J and Lo, C-Y and Wang,
T-Y and Kuo, C-HS and Lin, S-M and Chung, F-T and Lin, H-C and Hsieh, M-H and Lo, Y-L},
Title = {Cardiorespiratory coupling is associated with exercise
capacity in patients with chronic obstructive pulmonary
disease.},
Journal = {BMC pulmonary medicine},
Volume = {21},
Number = {1},
Pages = {22},
Year = {2021},
Month = {January},
url = {http://dx.doi.org/10.1186/s12890-021-01400-1},
Abstract = {<h4>Background</h4>The interaction between the pulmonary
function and cardiovascular mechanics is a crucial issue,
particularly when treating patients with chronic obstructive
pulmonary disease (COPD). Synchrogram index is a new
parameter that can quantify this interaction and has the
potential to apply in COPD patients. Our objective in this
study was to characterize cardiorespiratory interactions in
terms of cardiorespiratory coupling (CRC) using the
synchrogram index of the heart rate and respiratory flow
signals in patients with chronic obstructive pulmonary
disease.<h4>Methods</h4>This is a cross-sectional and
preliminary data from a prospective study, which examines 55
COPD patients. K-means clustering analysis was applied to
cluster COPD patients based on the synchrogram index. Linear
regression and multivariable regression analysis were used
to determine the correlation between the synchrogram index
and the exercise capacity assessed by a six-minute walking
test (6MWT).<h4>Results</h4>The 55 COPD patients were
separated into a synchronized group (median 0.89
(0.64-0.97), n = 43) and a desynchronized group (median
0.23 (0.02-0.51), n = 12) based on K-means clustering
analysis. Synchrogram index was correlated significantly
with six minutes walking distance (r = 0.42,
p = 0.001) and distance saturation product
(r = 0.41, p = 0.001) assessed by 6MWT, and still
was an independent variable by multivariable regression
analysis.<h4>Conclusion</h4>This is the first result
studying the heart-lung interaction in terms of
cardiorespiratory coupling in COPD patients by the
synchrogram index, and COPD patients are clustered into
synchronized and desynchronized groups. Cardiorespiratory
coupling is associated with exercise capacity in patients
with COPD.},
Doi = {10.1186/s12890-021-01400-1},
Key = {fds354951}
}
@article{fds355816,
Author = {Liu, GR and Lo, YL and Sheu, YC and Wu, HT},
Title = {Explore Intrinsic Geometry of Sleep Dynamics and Predict
Sleep Stage by Unsupervised Learning Techniques},
Volume = {168},
Pages = {279-324},
Booktitle = {Springer Optimization and Its Applications},
Year = {2021},
Month = {January},
url = {http://dx.doi.org/10.1007/978-3-030-61887-2_11},
Abstract = {We propose a novel unsupervised approach for sleep dynamics
exploration and automatic annotation by combining modern
harmonic analysis tools. Specifically, we apply
diffusion-based algorithms, diffusion map (DM), and
alternating diffusion (AD) algorithms, to reconstruct the
intrinsic geometry of sleep dynamics by reorganizing the
spectral information of an electroencephalogram (EEG)
extracted from a nonlinear-type time frequency analysis
tool, the synchrosqueezing transform (SST). The
visualization is achieved by the nonlinear dimension
reduction properties of DM and AD. Moreover, the
reconstructed nonlinear geometric structure of the sleep
dynamics allows us to achieve the automatic annotation
purpose. The hidden Markov model is trained to predict the
sleep stage. The prediction performance is validated on a
publicly available benchmark database, Physionet Sleep-EDF
[extended] SC∗ and ST∗, with the leave-one-subject-out
cross-validation. The overall accuracy and macro F1 achieve
82.57% and 76% in Sleep-EDF SC∗ and 77.01% and 71.53% in
Sleep-EDF ST∗, which is compatible with the
state-of-the-art results by supervised learning-based
algorithms. The results suggest the potential of the
proposed algorithm for clinical applications.},
Doi = {10.1007/978-3-030-61887-2_11},
Key = {fds355816}
}
@article{fds361918,
Author = {Chen, Y-C and Wu, H-T and Tu, P-H and Yeh, C-H and Liu, T-C and Yeap, M-C and Chao, Y-P and Chen, P-L and Lu, C-S and Chen, C-C},
Title = {Theta Oscillations at Subthalamic Region Predicts Hypomania
State After Deep Brain Stimulation in Parkinson's
Disease.},
Journal = {Frontiers in human neuroscience},
Volume = {15},
Pages = {797314},
Year = {2021},
Month = {January},
url = {http://dx.doi.org/10.3389/fnhum.2021.797314},
Abstract = {Subthalamic nucleus (STN) deep brain stimulation (DBS) is an
effective treatment for the motor impairments of patients
with advanced Parkinson's disease. However, mood or
behavioral changes, such as mania, hypomania, and impulsive
disorders, can occur postoperatively. It has been suggested
that these symptoms are associated with the stimulation of
the limbic subregion of the STN. Electrophysiological
studies demonstrate that the low-frequency activities in
ventral STN are modulated during emotional processing. In
this study, we report 22 patients with Parkinson's disease
who underwent STN DBS for treatment of motor impairment and
presented stimulation-induced mood elevation during initial
postoperative programming. The contact at which a euphoric
state was elicited by stimulation was termed as the
hypomania-inducing contact (HIC) and was further correlated
with intraoperative local field potential recorded during
the descending of DBS electrodes. The power of four
frequency bands, namely, θ (4-7 Hz), α (7-10 Hz), β
(13-35 Hz), and γ (40-60 Hz), were determined by a
non-linear variation of the spectrogram using the
concentration of frequency of time (conceFT). The depth of
maximum θ power is located approximately 2 mm below HIC on
average and has significant correlation with the location of
contacts (<i>r</i> = 0.676, <i>p</i> < 0.001), even after
partializing the effect of α and β, respectively (<i>r</i>
= 0.474, <i>p</i> = 0.022; <i>r</i> = 0.461, <i>p</i> =
0.027). The occurrence of HIC was not associated with
patient-specific characteristics such as age, gender,
disease duration, motor or non-motor symptoms before the
operation, or improvement after stimulation. Taken together,
these data suggest that the location of maximum θ power is
associated with the stimulation-induced hypomania and the
prediction of θ power is frequency specific. Our results
provide further information to refine targeting
intraoperatively and select stimulation contacts in
programming.},
Doi = {10.3389/fnhum.2021.797314},
Key = {fds361918}
}
@article{fds363034,
Author = {Meynard, A and Seneviratna, G and Doyle, E and Becker, J and Wu, HT and Borg, JS},
Title = {Predicting Trust Using Automated Assessment of Multivariate
Interactional Synchrony},
Journal = {Proceedings - 2021 16th IEEE International Conference on
Automatic Face and Gesture Recognition, FG
2021},
Year = {2021},
Month = {January},
ISBN = {9781665431767},
url = {http://dx.doi.org/10.1109/FG52635.2021.9667082},
Abstract = {Diverse disciplines are interested in how the coordination
of interacting agents' movements, emotions, and physiology
over time impacts social behavior. Here, we describe a new
multivariate procedure for automating the investigation of
this kind of behaviorally-relevant 'interactional
synchrony', and introduce a novel interactional synchrony
measure based on features of dynamic time warping (DTW)
paths. We demonstrate that our DTW path-based measure of
interactional synchrony between facial action units of two
people interacting freely in a natural social interaction
can be used to predict how much trust they will display in a
subsequent Trust Game. We also show that our approach
outperforms univariate head movement models, models that
consider participants' facial action units independently,
and models that use previously proposed synchrony or
similarity measures. The insights of this work can be
applied to any research question that aims to quantify the
temporal coordination of multiple signals over time, but has
immediate applications in psychology, medicine, and
robotics.},
Doi = {10.1109/FG52635.2021.9667082},
Key = {fds363034}
}
@article{fds355196,
Author = {Malik, J and Loring, Z and Piccini, JP and Wu, H-T},
Title = {Interpretable morphological features for efficient
single-lead automatic ventricular ectopy
detection.},
Journal = {J Electrocardiol},
Volume = {65},
Pages = {55-63},
Year = {2021},
url = {http://dx.doi.org/10.1016/j.jelectrocard.2020.11.014},
Abstract = {OBJECTIVE: We designed an automatic, computationally
efficient, and interpretable algorithm for detecting
ventricular ectopic beats in long-term, single-lead
electrocardiogram recordings. METHODS: We built five simple,
interpretable, and computationally efficient features from
each cardiac cycle, including a novel morphological feature
which described the distance to the median beat in the
recording. After an unsupervised subject-specific
normalization procedure, we trained an ensemble binary
classifier using the AdaBoost algorithm RESULTS: After our
classifier was trained on subset DS1 of the Massachusetts
Institute of Technology-Beth Israel Hospital (MIT-BIH)
Arrhythmia database, our classifier obtained an F1 score of
94.35% on subset DS2 of the same database. The same
classifier achieved F1 scores of 92.06% on the St.
Petersburg Institute of Cardiological Technics (INCART)
12-lead Arrhythmia database and 91.40% on the MIT-BIH
Long-term database. A phenotype-specific analysis of model
performance was afforded by the annotations included in the
St. Petersburg INCART Arrhythmia database CONCLUSION: The
five features this novel algorithm employed allowed our
ventricular ectopy detector to obtain high precision on
previously unseen subjects and databases SIGNIFICANCE: Our
ventricular ectopy detector will be used to study the
relationship between premature ventricular contractions and
adverse patient outcomes such as congestive heart failure
and death.},
Doi = {10.1016/j.jelectrocard.2020.11.014},
Key = {fds355196}
}
@article{fds354212,
Author = {Su, P-C and Soliman, EZ and Wu, H-T},
Title = {Robust T-End Detection via T-End Signal Quality Index and
Optimal Shrinkage.},
Journal = {Sensors (Basel, Switzerland)},
Volume = {20},
Number = {24},
Pages = {E7052},
Year = {2020},
Month = {December},
url = {http://dx.doi.org/10.3390/s20247052},
Abstract = {An automatic accurate T-wave end (T-end) annotation for the
electrocardiogram (ECG) has several important clinical
applications. While there have been several algorithms
proposed, their performance is usually deteriorated when the
signal is noisy. Therefore, we need new techniques to
support the noise robustness in T-end detection. We propose
a new algorithm based on the signal quality index (SQI) for
T-end, coined as tSQI, and the optimal shrinkage (OS). For
segments with low tSQI, the OS is applied to enhance the
signal-to-noise ratio (SNR). We validated the proposed
method using eleven short-term ECG recordings from QT
database available at Physionet, as well as four 14-day ECG
recordings which were visually annotated at a central ECG
core laboratory. We evaluated the correlation between the
real-world signal quality for T-end and tSQI, and the
robustness of proposed algorithm to various additive noises
of different types and SNR's. The performance of proposed
algorithm on arrhythmic signals was also illustrated on
MITDB arrhythmic database. The labeled signal quality is
well captured by tSQI, and the proposed OS denoising help
stabilize existing T-end detection algorithms under noisy
situations by making the mean of detection errors decrease.
Even when applied to ECGs with arrhythmia, the proposed
algorithm still performed well if proper metric is applied.
We proposed a new T-end annotation algorithm. The efficiency
and accuracy of our algorithm makes it a good fit for
clinical applications and large ECG databases. This study is
limited by the small size of annotated datasets.},
Doi = {10.3390/s20247052},
Key = {fds354212}
}
@article{fds366031,
Author = {Sourisseau, M and Wang, YG and Womersley, RS and Wu, H-T and Yu,
W-H},
Title = {Improve Concentration of Frequency and Time (Conceft) by
Novel Complex Spherical Designs},
Year = {2020},
Month = {November},
url = {http://dx.doi.org/10.1101/2020.11.23.394007},
Abstract = {<jats:title>A<jats:sc>bstract</jats:sc></jats:title><jats:p>Concentration
of frequency and time (ConceFT) is a generalized multitaper
algorithm introduced to analyze complicated non-stationary
time series. To avoid the randomness in the original ConceFT
algorithm, we apply the novel complex spherical design
technique to standardize ConceFT, which we coin
<jats:italic>CQU-ConceFT.</jats:italic> The proposed
CQU-ConceFT is applied to visualize the spindle structure in
the electroencephalogram signal during the N2 sleep stage
and other physiological time series.</jats:p>},
Doi = {10.1101/2020.11.23.394007},
Key = {fds366031}
}
@article{fds361595,
Author = {Ding, X and Wu, H-T},
Title = {Impact of signal-to-noise ratio and bandwidth on graph
Laplacian spectrum from high-dimensional noisy point
cloud},
Year = {2020},
Month = {November},
Abstract = {We systematically study the spectrum of kernel-based graph
Laplacian (GL) constructed from high-dimensional and noisy
random point cloud in the nonnull setup. The problem is
motived by studying the model when the clean signal is
sampled from a manifold that is embedded in a
low-dimensional Euclidean subspace, and corrupted by
high-dimensional noise. We quantify how the signal and noise
interact over different regions of signal-to-noise ratio
(SNR), and report the resulting peculiar spectral behavior
of GL. In addition, we explore the impact of chosen kernel
bandwidth on the spectrum of GL over different regions of
SNR, which lead to an adaptive choice of kernel bandwidth
that coincides with the common practice in real data. This
result paves the way to a theoretical understanding of how
practitioners apply GL when the dataset is
noisy.},
Key = {fds361595}
}
@article{fds337127,
Author = {Frasch, MG and Lobmaier, SM and Stampalija, T and Desplats, P and Pallarés, ME and Pastor, V and Brocco, MA and Wu, H-T and Schulkin, J and Herry, CL and Seely, AJE and Metz, GAS and Louzoun, Y and Antonelli,
MC},
Title = {Non-invasive biomarkers of fetal brain development
reflecting prenatal stress: An integrative multi-scale
multi-species perspective on data collection and
analysis.},
Journal = {Neuroscience and biobehavioral reviews},
Volume = {117},
Pages = {165-183},
Year = {2020},
Month = {October},
url = {http://dx.doi.org/10.1016/j.neubiorev.2018.05.026},
Abstract = {Prenatal stress (PS) impacts early postnatal behavioural and
cognitive development. This process of 'fetal programming'
is mediated by the effects of the prenatal experience on the
developing hypothalamic-pituitary-adrenal (HPA) axis and
autonomic nervous system (ANS). We derive a multi-scale
multi-species approach to devising preclinical and clinical
studies to identify early non-invasively available pre- and
postnatal biomarkers of PS. The multiple scales include
brain epigenome, metabolome, microbiome and the ANS activity
gauged via an array of advanced non-invasively obtainable
properties of fetal heart rate fluctuations. The proposed
framework has the potential to reveal mechanistic links
between maternal stress during pregnancy and changes across
these physiological scales. Such biomarkers may hence be
useful as early and non-invasive predictors of
neurodevelopmental trajectories influenced by the PS as well
as follow-up indicators of success of therapeutic
interventions to correct such altered neurodevelopmental
trajectories. PS studies must be conducted on multiple
scales derived from concerted observations in multiple
animal models and human cohorts performed in an interactive
and iterative manner and deploying machine learning for data
synthesis, identification and validation of the best
non-invasive detection and follow-up biomarkers, a
prerequisite for designing effective therapeutic
interventions.},
Doi = {10.1016/j.neubiorev.2018.05.026},
Key = {fds337127}
}
@article{fds352642,
Author = {Wu, HT},
Title = {Current state of nonlinear-type time–frequency analysis
and applications to high-frequency biomedical
signals},
Journal = {Current Opinion in Systems Biology},
Volume = {23},
Pages = {8-21},
Year = {2020},
Month = {October},
url = {http://dx.doi.org/10.1016/j.coisb.2020.07.013},
Abstract = {Motivated by analyzing complicated time series,
nonlinear-type time–frequency analysis has become an
active research topic in the past decades. Those developed
tools have been applied to various problems. In this
article, we review those developed tools and summarize their
applications to high-frequency biomedical signals. They are
applied to extract useful features from the signal or
quantify its dynamical behavior for the subsequent
statistical analysis.},
Doi = {10.1016/j.coisb.2020.07.013},
Key = {fds352642}
}
@article{fds352988,
Author = {Chang, Z and Chen, Z and Stephen, CD and Schmahmann, JD and Wu, H-T and Sapiro, G and Gupta, AS},
Title = {Accurate detection of cerebellar smooth pursuit eye movement
abnormalities via mobile phone video and machine
learning.},
Journal = {Scientific reports},
Volume = {10},
Number = {1},
Pages = {18641},
Year = {2020},
Month = {October},
url = {http://dx.doi.org/10.1038/s41598-020-75661-x},
Abstract = {Eye movements are disrupted in many neurodegenerative
diseases and are frequent and early features in conditions
affecting the cerebellum. Characterizing eye movements is
important for diagnosis and may be useful for tracking
disease progression and response to therapies. Assessments
are limited as they require an in-person evaluation by a
neurology subspecialist or specialized and expensive
equipment. We tested the hypothesis that important eye
movement abnormalities in cerebellar disorders (i.e.,
ataxias) could be captured from iPhone video. Videos of the
face were collected from individuals with ataxia
(n = 102) and from a comparative population (Parkinson's
disease or healthy participants, n = 61). Computer
vision algorithms were used to track the position of the eye
which was transformed into high temporal resolution spectral
features. Machine learning models trained on eye movement
features were able to identify abnormalities in smooth
pursuit (a key eye behavior) and accurately distinguish
individuals with abnormal pursuit from controls
(sensitivity = 0.84, specificity = 0.77). A novel
machine learning approach generated severity estimates that
correlated well with the clinician scores. We demonstrate
the feasibility of capturing eye movement information using
an inexpensive and widely accessible technology. This may be
a useful approach for disease screening and for measuring
severity in clinical trials.},
Doi = {10.1038/s41598-020-75661-x},
Key = {fds352988}
}
@article{fds353053,
Author = {Chang, H-C and Wu, H-T and Huang, P-C and Ma, H-P and Lo, Y-L and Huang,
Y-H},
Title = {Portable Sleep Apnea Syndrome Screening and Event Detection
Using Long Short-Term Memory Recurrent Neural
Network.},
Journal = {Sensors (Basel, Switzerland)},
Volume = {20},
Number = {21},
Pages = {E6067},
Year = {2020},
Month = {October},
url = {http://dx.doi.org/10.3390/s20216067},
Abstract = {Obstructive sleep apnea/hypopnea syndrome (OSAHS) is
characterized by repeated airflow partial reduction or
complete cessation due to upper airway collapse during
sleep. OSAHS can induce frequent awake and intermittent
hypoxia that is associated with hypertension and
cardiovascular events. Full-channel Polysomnography (PSG) is
the gold standard for diagnosing OSAHS; however, this PSG
evaluation process is unsuitable for home screening. To
solve this problem, a measuring module integrating abdominal
and thoracic triaxial accelerometers, a pulsed oximeter
(SpO2) and an electrocardiogram sensor was devised in this
study. Moreover, a long short-term memory recurrent neural
network model is proposed to classify four types of sleep
breathing patterns, namely obstructive sleep apnea (OSA),
central sleep apnea (CSA), hypopnea (HYP) events and normal
breathing (NOR). The proposed algorithm not only reports the
apnea-hypopnea index (AHI) through the acquired overnight
signals but also identifies the occurrences of OSA, CSA, HYP
and NOR, which assists in OSAHS diagnosis. In the clinical
experiment with 115 participants, the performances of the
proposed system and algorithm were compared with those of
traditional expert interpretation based on PSG signals. The
accuracy of AHI severity group classification was 89.3%, and
the AHI difference for PSG expert interpretation was
5.0±4.5. The overall accuracy of detecting abnormal OSA,
CSA and HYP events was 92.3%.},
Doi = {10.3390/s20216067},
Key = {fds353053}
}
@article{fds345878,
Author = {Chang, C-H and Fang, Y-L and Wang, Y-J and Wu, H-T and Lin,
Y-T},
Title = {Differentiation of skin incision and laparoscopic trocar
insertion via quantifying transient bradycardia measured by
electrocardiogram.},
Journal = {Journal of clinical monitoring and computing},
Volume = {34},
Number = {4},
Pages = {753-762},
Year = {2020},
Month = {August},
url = {http://dx.doi.org/10.1007/s10877-019-00378-w},
Abstract = {Most surgical procedures involve structures deeper than the
skin. However, the difference in surgical noxious
stimulation between skin incision and laparoscopic trocar
insertion is unknown. By analyzing instantaneous heart rate
(IHR) calculated from the electrocardiogram, in particular
the transient bradycardia in response to surgical stimuli,
this study investigates surgical noxious stimuli arising
from skin incision and laparoscopic trocar insertion, and
their difference. Thirty-five patients undergoing
laparoscopic cholecystectomy were enrolled in this
prospective observational study. Sequential surgical steps
including umbilical skin incision (11 mm), umbilical trocar
insertion (11 mm), xiphoid skin incision (5 mm), xiphoid
trocar insertion (5 mm), subcostal skin incision (3 mm),
and subcostal trocar insertion (3 mm) were investigated.
IHR was derived from electrocardiography and calculated by
the modern time-varying power spectrum. Similar to the
classical heart rate variability analysis, the time-varying
low frequency power (tvLF), time-varying high frequency
power (tvHF), and tvLF-to-tvHF ratio (tvLHR) were
calculated. Prediction probability (P<sub>K</sub>) analysis
and global pointwise F-test were used to compare the
statistical performance between indices and the heart rate
readings from the patient monitor. Analysis of IHR showed
that surgical stimulus elicits a transient bradycardia,
followed by the increase of heart rate. Transient
bradycardia is more significant in trocar insertion than
skin incision (p < 0.001 for tvHF). The IHR change
quantifies differential responses to different surgical
intensity. Serial P<sub>K</sub> analysis demonstrates
de-sensitization in skin incision, but not in laparoscopic
trocar insertion. Quantitative indices present the transient
bradycardia introduced by noxious stimulation. The results
indicate different effects between skin incision and trocar
insertion.},
Doi = {10.1007/s10877-019-00378-w},
Key = {fds345878}
}
@article{fds361596,
Author = {McErlean, J and Malik, J and Lin, Y-T and Talmon, R and Wu,
H-T},
Title = {Unsupervised Ensembling of Multiple Software Sensors with
Phase Synchronization: A Robust approach For
Electrocardiogram-derived Respiration},
Year = {2020},
Month = {June},
Abstract = {Objective: We aimed to fuse the outputs of different
electrocardiogram-derived respiration (EDR) algorithms to
create one EDR signal that is of higher quality. Methods: We
viewed each EDR algorithm as a software sensor that recorded
breathing activity from a different vantage point,
identified high-quality software sensors based on the
respiratory signal quality index, aligned the
highest-quality EDRs with a phase synchronization technique
based on the graph connection Laplacian, and finally fused
those aligned, high-quality EDRs. We refer to the output as
the sync-ensembled EDR signal. The proposed algorithm was
evaluated on two large-scale databases of whole-night
polysomnograms. We evaluated the performance of the proposed
algorithm using three respiratory signals recorded from
different hardware sensors, and compared it with other
existing EDR algorithms. A sensitivity analysis was carried
out for a total of five cases: fusion by taking the mean of
EDR signals, and the four cases of EDR signal alignment
without and with synchronization and without and with signal
quality selection. Results: The sync-ensembled EDR algorithm
outperforms existing EDR algorithms when evaluated by the
synchronized correlation (-score), optimal transport (OT)
distance, and average frequency (AF) score, all with
statistical significance. The sensitivity analysis shows
that the signal quality selection and EDR signal alignment
are both critical for the performance, both with statistical
significance. Conclusion: The sync-ensembled EDR provides
robust respiratory information from electrocardiogram.
Significance: Phase synchronization is not only
theoretically rigorous but also practical to design a robust
EDR.},
Key = {fds361596}
}
@article{fds361418,
Author = {Cicone, A and Wu, H-T},
Title = {Convergence analysis of Adaptive Locally Iterative Filtering
and SIFT method},
Year = {2020},
Month = {May},
Abstract = {Adaptive Local Iterative Filtering (ALIF) is a currently
proposed novel time-frequency analysis tool. It has been
empirically shown that ALIF is able to separate components
and overcome the mode-mixing problem. However, so far its
convergence is still an open problem, particularly for
highly nonstationary signals, due to the fact that the
kernel associated with ALIF is non-translational invariant,
non-convolutional and non-symmetric. Our first contribution
in this work is providing a convergence analysis of ALIF.
From the practical perspective, ALIF depends on a robust
frequencies estimator, based on which the decomposition can
be achieved. Our second contribution is proposing a robust
and adaptive decomposition method for noisy and
nonstationary signals, which we coined the Synchrosqueezing
Iterative Filtering Technique (SIFT). In SIFT, we apply the
synchrosqueezing transform to estimate the instantaneous
frequency, and then apply the ALIF to decompose a signal. We
show numerically the ability of this new approach in
handling highly nonstationary signals.},
Key = {fds361418}
}
@article{fds349536,
Author = {Malik, J and Soliman, EZ and Wu, H-T},
Title = {An adaptive QRS detection algorithm for ultra-long-term ECG
recordings.},
Journal = {Journal of electrocardiology},
Volume = {60},
Pages = {165-171},
Year = {2020},
Month = {May},
url = {http://dx.doi.org/10.1016/j.jelectrocard.2020.02.016},
Abstract = {<h4>Background</h4>Accurate detection of QRS complexes
during mobile, ultra-long-term ECG monitoring is challenged
by instances of high heart rate, dramatic and persistent
changes in signal amplitude, and intermittent deformations
in signal quality that arise due to subject motion,
background noise, and misplacement of the ECG
electrodes.<h4>Purpose</h4>We propose a revised QRS
detection algorithm which addresses the above-mentioned
challenges.<h4>Methods and results</h4>Our proposed
algorithm is based on a state-of-the-art algorithm after
applying two key modifications. The first modification is
implementing local estimates for the amplitude of the
signal. The second modification is a mechanism by which the
algorithm becomes adaptive to changes in heart rate. We
validated our proposed algorithm against the
state-of-the-art algorithm using short-term ECG recordings
from eleven annotated databases available at Physionet, as
well as four ultra-long-term (14-day) ECG recordings which
were visually annotated at a central ECG core laboratory. On
the database of ultra-long-term ECG recordings, our proposed
algorithm showed a sensitivity of 99.90% and a positive
predictive value of 99.73%. Meanwhile, the state-of-the-art
QRS detection algorithm achieved a sensitivity of 99.30% and
a positive predictive value of 99.68% on the same database.
The numerical efficiency of our new algorithm was evident,
as a 14-day recording sampled at 200 Hz was analyzed in
approximately 157 s.<h4>Conclusions</h4>We developed a new
QRS detection algorithm. The efficiency and accuracy of our
algorithm makes it a good fit for mobile health
applications, ultra-long-term and pathological ECG
recordings, and the batch processing of large ECG
databases.},
Doi = {10.1016/j.jelectrocard.2020.02.016},
Key = {fds349536}
}
@article{fds349332,
Author = {Wang, S-C and Wu, H-T and Huang, P-H and Chang, C-H and Ting, C-K and Lin,
Y-T},
Title = {Novel Imaging Revealing Inner Dynamics for Cardiovascular
Waveform Analysis via Unsupervised Manifold
Learning.},
Journal = {Anesthesia and analgesia},
Volume = {130},
Number = {5},
Pages = {1244-1254},
Year = {2020},
Month = {May},
url = {http://dx.doi.org/10.1213/ane.0000000000004738},
Abstract = {<h4>Background</h4>Cardiovascular waveforms contain
information for clinical diagnosis. By learning and
organizing the subtle change of waveform morphology from
large amounts of raw waveform data, unsupervised manifold
learning helps delineate a high-dimensional structure and
display it as a novel 3-dimensional (3D) image. We
hypothesize that the shape of this structure conveys
clinically relevant inner dynamics information.<h4>Methods</h4>To
validate this hypothesis, we investigate the
electrocardiography (ECG) waveform for ischemic heart
disease and arterial blood pressure (ABP) waveform in
dynamic vasoactive episodes. We model each beat or pulse to
be a point lying on a manifold-like a surface-and use the
diffusion map (DMap) to establish the relationship among
those pulses. The output of the DMap is converted to a 3D
image for visualization. For ECG datasets, first we analyzed
the non-ST-elevation ECG waveform distribution from unstable
angina to healthy control in the 3D image, and we
investigated intraoperative ST-elevation ECG waveforms to
show the dynamic ECG waveform changes. For ABP datasets, we
analyzed waveforms collected under endotracheal intubation
and administration of vasodilator. To quantify the dynamic
separation, we applied the support vector machine (SVM)
analysis and reported the total accuracy and macro-F1 score.
We further performed the trajectory analysis and derived the
moving direction of successive beats (or pulses) as vectors
in the high-dimensional space.<h4>Results</h4>For the
non-ST-elevation ECG, a hierarchical tree structure
comprising consecutive ECG waveforms spanning from unstable
angina to healthy control is presented in the 3D image
(accuracy = 97.6%, macro-F1 = 96.1%). The DMap helps
quantify and visualize the evolving direction of
intraoperative ST-elevation myocardial episode in a 1-hour
period (accuracy = 97.58%, macro-F1 = 96.06%). The ABP
waveform analysis of Nicardipine administration shows
interindividual difference (accuracy = 95.01%, macro-F1 =
96.9%) and their common directions from intraindividual
moving trajectories. The dynamic change of the ABP waveform
during endotracheal intubation shows a loop-like trajectory
structure, which can be further divided using the manifold
learning knowledge obtained from Nicardipine.<h4>Conclusions</h4>The
DMap and the generated 3D image of ECG or ABP waveforms
provides clinically relevant inner dynamics information. It
provides clues of acute coronary syndrome diagnosis, shows
clinical course in myocardial ischemic episode, and reveals
underneath physiological mechanism under stress or
vasodilators.},
Doi = {10.1213/ane.0000000000004738},
Key = {fds349332}
}
@article{fds349387,
Author = {Liu, G-R and Lustenberger, C and Lo, Y-L and Liu, W-T and Sheu, Y-C and Wu,
H-T},
Title = {Save Muscle Information-Unfiltered EEG Signal Helps
Distinguish Sleep Stages.},
Journal = {Sensors (Basel, Switzerland)},
Volume = {20},
Number = {7},
Pages = {E2024},
Year = {2020},
Month = {April},
url = {http://dx.doi.org/10.3390/s20072024},
Abstract = {Based on the well-established biopotential theory, we
hypothesize that the high frequency spectral information,
like that higher than 100Hz, of the EEG signal recorded in
the off-the-shelf EEG sensor contains muscle tone
information. We show that an existing automatic sleep stage
annotation algorithm can be improved by taking this
information into account. This result suggests that if
possible, we should sample the EEG signal with a high
sampling rate, and preserve as much spectral information as
possible.},
Doi = {10.3390/s20072024},
Key = {fds349387}
}
@article{fds361505,
Author = {Chen, Z and Wu, H-T},
Title = {When Ramanujan meets time-frequency analysis in complicated
time series analysis},
Journal = {Pure Appl. Analysis},
Volume = {4},
Pages = {629-673},
Year = {2020},
Month = {March},
Abstract = {To handle time series with complicated oscillatory
structure, we propose a novel time-frequency (TF) analysis
tool that fuses the short time Fourier transform (STFT) and
periodic transform (PT). Since many time series oscillate
with time-varying frequency, amplitude and non-sinusoidal
oscillatory pattern, a direct application of PT or STFT
might not be suitable. However, we show that by combining
them in a proper way, we obtain a powerful TF analysis tool.
We first combine the Ramanujan sums and $l_1$ penalization
to implement the PT. We call the algorithm Ramanujan PT
(RPT). The RPT is of its own interest for other
applications, like analyzing short signal composed of
components with integer periods, but that is not the focus
of this paper. Second, the RPT is applied to modify the STFT
and generate a novel TF representation of the complicated
time series that faithfully reflect the instantaneous
frequency information of each oscillatory components. We
coin the proposed TF analysis the Ramanujan de-shape (RDS)
and vectorized RDS (vRDS). In addition to showing some
preliminary analysis results on complicated biomedical
signals, we provide theoretical analysis about RPT.
Specifically, we show that the RPT is robust to three
commonly encountered noises, including envelop fluctuation,
jitter and additive noise.},
Key = {fds361505}
}
@article{fds348869,
Author = {Liu, Y-W and Kao, S-L and Wu, H-T and Liu, T-C and Fang, T-Y and Wang,
P-C},
Title = {Transient-evoked otoacoustic emission signals predicting
outcomes of acute sensorineural hearing loss in patients
with Ménière's disease.},
Journal = {Acta oto-laryngologica},
Volume = {140},
Number = {3},
Pages = {230-235},
Year = {2020},
Month = {March},
url = {http://dx.doi.org/10.1080/00016489.2019.1704865},
Abstract = {<b>Background:</b> Fluctuating hearing loss is
characteristic of Ménière's disease (MD) during acute
episodes. However, no reliable audiometric hallmarks are
available for counselling the hearing recovery
possibility.<b>Aims/objectives:</b> To find parameters for
predicting MD hearing outcomes.<b>Material and methods:</b>
We applied machine learning techniques to analyse
transient-evoked otoacoustic emission (TEOAE) signals
recorded from patients with MD. Thirty unilateral MD
patients were recruited prospectively after onset of acute
cochleo-vestibular symptoms. Serial TEOAE and pure-tone
audiogram (PTA) data were recorded longitudinally. Denoised
TEOAE signals were projected onto the three most prominent
principal directions through a linear transformation. Binary
classification was performed using a support vector machine
(SVM). TEOAE signal parameters, including signal energy and
group delay, were compared between improved (PTA
improvement: ≥15 dB) and nonimproved groups using
Welch's t-test.<b>Results:</b> Signal energy did not differ
(<i>p</i> = .64) but a significant difference in 1-kHz
(<i>p</i> = .045) group delay was recorded between
improved and nonimproved groups. The SVM achieved a
cross-validated accuracy of >80% in predicting hearing
outcomes.<b>Conclusions and significance:</b> This study
revealed that baseline TEOAE parameters obtained during
acute MD episodes, when processed through machine learning
technology, may provide information on outer hair cell
function to predict hearing recovery.},
Doi = {10.1080/00016489.2019.1704865},
Key = {fds348869}
}
@article{fds341502,
Author = {Lo, Y-L and Wu, H-T and Lin, Y-T and Kuo, H-P and Lin,
T-Y},
Title = {Hypoventilation patterns during bronchoscopic sedation and
their clinical relevance based on capnographic and
respiratory impedance analysis.},
Journal = {Journal of clinical monitoring and computing},
Volume = {34},
Number = {1},
Pages = {171-179},
Year = {2020},
Month = {February},
url = {http://dx.doi.org/10.1007/s10877-019-00269-0},
Abstract = {Capnography involves the measurement of end-tidal
CO<sub>2</sub> (EtCO<sub>2</sub>) values to detect
hypoventilation in patients undergoing sedation. In a
previous study, we reported that initiating a flexible
bronchoscopy (FB) examination only after detecting signs of
hypoventilation could reduce the risk of hypoxemia without
compromising the tolerance of the patient for this type of
intervention. We hypothesize that hypoventilation status
could be determined with greater precision by combining
thoracic impedance-based respiratory signals, RESP, and
EtCO<sub>2</sub> signals obtained from a nasal-oral cannula.
Retrospective analysis was conducted on RESP and
EtCO<sub>2</sub> waveforms obtained from patients during the
induction of sedation using propofol for bronchoscopic
examination in a previous study. EtCO<sub>2</sub> waveforms
associated with hypoventilation were then compared with RESP
patterns, patient variables, and sedation outcomes. Signals
suitable for analysis were obtained from 44 subjects, 42 of
whom presented indications of hypoventilation, as determined
by EtCO<sub>2</sub> waveforms. Two subtypes of
hypoventilation were identified by RESP: central-predominant
(n = 22, flat line RESP pattern) and
non-central-predominant (n = 20, RESP pattern indicative
of respiratory effort with upper airway collapse). Compared
to cases of non-central-predominant hypoventilation, those
presenting central-predominant hypoventilation during
induction were associated with a lower propofol dose
(40.2 ± 18.3 vs. 60.8 ± 26.1 mg, p = 0.009),
a lower effect site concentration of propofol
(2.02 ± 0.33 vs. 2.38 ± 0.44 µg/ml,
p = 0.01), more rapid induction (146.1 ± 105.5 vs.
260.9 ± 156.2 s, p = 0.01), and lower total
propofol dosage (96.6 ± 41.7 vs. 130.6 ± 53.4 mg,
p = 0.04). Hypoventilation status (as revealed by
EtCO<sub>2</sub> levels) could be further classified by RESP
into central-predominant or non-central-predominant types.
It appears that patients with central-predominant
hypoventilation are more sensitive to propofol during the
induction of sedation. RESP values could be used to tailor
sedation management specifically to individual
patients.},
Doi = {10.1007/s10877-019-00269-0},
Key = {fds341502}
}
@article{fds347336,
Author = {Lobmaier, SM and Müller, A and Zelgert, C and Shen, C and Su, PC and Schmidt, G and Haller, B and Berg, G and Fabre, B and Weyrich, J and Wu,
HT and Frasch, MG and Antonelli, MC},
Title = {Fetal heart rate variability responsiveness to maternal
stress, non-invasively detected from maternal transabdominal
ECG.},
Journal = {Archives of gynecology and obstetrics},
Volume = {301},
Number = {2},
Pages = {405-414},
Year = {2020},
Month = {February},
url = {http://dx.doi.org/10.1007/s00404-019-05390-8},
Abstract = {<h4>Purpose</h4>Prenatal stress (PS) during pregnancy
affects in utero- and postnatal child brain-development. Key
systems affected are the hypothalamic-pituitary-adrenal axis
and the autonomic nervous system (ANS). Maternal- and fetal
ANS activity can be gauged non-invasively from
transabdominal electrocardiogram (taECG). We propose a novel
approach to assess couplings between maternal (mHR) and
fetal heart rate (fHR) as a new biomarker for PS based on
bivariate phase-rectified signal averaging (BPRSA). We
hypothesized that PS exerts lasting impact on
fHR.<h4>Methods</h4>Prospective case-control study matched
for maternal age, parity, and gestational age during the
third trimester using the Cohen Perceived Stress Scale
(PSS-10) questionnaire with PSS-10 over or equal 19
classified as stress group (SG). Women with PSS-10 < 19
served as control group (CG). Fetal electrocardiograms were
recorded by a taECG. Coupling between mHR and fHR was
analyzed by BPRSA resulting in fetal stress index (FSI).
Maternal hair cortisol, a memory of chronic stress exposure
for 2-3 months, was measured at birth.<h4>Results</h4>538/1500
pregnant women returned the questionnaire, 55/538 (10.2%)
mother-child pairs formed SG and were matched with 55/449
(12.2%) consecutive patients as CG. Maternal hair cortisol
was 86.6 (48.0-169.2) versus 53.0 (34.4-105.9) pg/mg
(p = 0.029). At 36 + 5 weeks, FSI was significantly
higher in fetuses of stressed mothers when compared to
controls [0.43 (0.18-0.85) versus 0.00 (- 0.49-0.18),
p < 0.001].<h4>Conclusion</h4>Prenatal maternal stress
affects the coupling between maternal and fetal heart rate
detectable non-invasively a month prior to birth. Lasting
effects on neurodevelopment of affected offspring should be
studied.<h4>Trial registration</h4>Clinical trial
registration: NCT03389178.},
Doi = {10.1007/s00404-019-05390-8},
Key = {fds347336}
}
@article{fds346863,
Author = {Liu, GR and Lo, YL and Malik, J and Sheu, YC and Wu,
HT},
Title = {Diffuse to fuse EEG spectra – Intrinsic geometry of sleep
dynamics for classification},
Journal = {Biomedical Signal Processing and Control},
Volume = {55},
Year = {2020},
Month = {January},
url = {http://dx.doi.org/10.1016/j.bspc.2019.101576},
Abstract = {We propose a novel algorithm for sleep dynamics
visualization and automatic annotation by applying diffusion
geometry based sensor fusion algorithm to fuse spectral
information from two electroencephalograms (EEG). The
diffusion geometry approach helps organize the nonlinear
dynamical structure hidden in the EEG signal. The
visualization is achieved by the nonlinear dimension
reduction capability of the chosen diffusion geometry
algorithms. For the automatic annotation purpose, the
support vector machine is trained to predict the sleep
stage. The prediction performance is validated on a publicly
available benchmark database, Physionet Sleep-EDF [extended]
SC* (SC = Sleep Cassette) and ST* (ST = Sleep Telemetry),
with the leave-one-subject-out cross validation. When we
have a single EEG channel (Fpz-Cz), the overall accuracy,
macro F1 and Cohen's kappa achieve 82.72%, 75.91% and 76.1%
respectively in Sleep-EDF SC* and 78.63%, 73.58% and 69.48%
in Sleep-EDF ST*. This performance is compatible with the
state-of-the-art results. When we have two EEG channels
(Fpz-Cz and Pz-Oz), the overall accuracy, macro F1 and
Cohen's kappa achieve 84.44%, 78.25% and 78.36% respectively
in Sleep-EDF SC* and 79.05%, 74.73% and 70.31% in Sleep-EDF
ST*. The results suggest the potential of the proposed
algorithm in practical applications.},
Doi = {10.1016/j.bspc.2019.101576},
Key = {fds346863}
}
@article{fds352989,
Author = {Huroyan, V and Lerman, G and Wu, H-T},
Title = {Solving Jigsaw Puzzles by the Graph Connection
Laplacian},
Journal = {SIAM Journal on Imaging Sciences},
Volume = {13},
Number = {4},
Pages = {1717-1753},
Publisher = {Society for Industrial & Applied Mathematics
(SIAM)},
Year = {2020},
Month = {January},
url = {http://dx.doi.org/10.1137/19m1290760},
Doi = {10.1137/19m1290760},
Key = {fds352989}
}
@article{fds353257,
Author = {Alian, A and Lo, Y-L and Shelley, K and Wu, H-T},
Title = {Reconsider phase reconstruction in signals with dynamic
periodicity from the modern signal processing
perspective},
Year = {2020},
url = {http://dx.doi.org/10.1101/2020.09.29.310417},
Abstract = {Phase is the most fundamental physical quantity when we
study an oscillatory time series. There are many tools
aiming to estimate phase, most of them are developed based
on the analytic function model. Unfortunately, this approach
might not be suitable for modern signals with intrinsic
nonstartionary structure , including multiple oscillatory
components, each with time-varying frequency, amplitude, and
non-sinusoidal oscillation, e.g., biomedical signals.
Specifically, due to the lack of consensus of model and
algorithm, phases estimated from signals simultaneously
recorded from different sensors for the same physiological
system from the same subject might be different. This fact
might challenge reproducibility, communication, and
scientific interpretation and thus we need a standardized
approach with theoretical support over a unified model. In
this paper, after summarizing existing models for phase and
discussing the main challenge caused by the above-mentioned
intrinsic nonstartionary structure, we introduce the
adaptive non-harmonic model (ANHM) , provide a definition of
phase called fundamental phase , which is a vector-valued
function describing the dynamics of all oscillatory
components in the signal, and suggest a time-varying
bandpass filter (tvBPF) scheme based on time-frequency
analysis tools to estimate the fundamental phase. The
proposed approach is validated with a simulated database and
a real-world database with experts’ labels, and it is
applied to two real-world databases, each of which has
biomedical signals recorded from different sensors, to show
how to standardize the definition of phase in the real-world
experimental environment. Specifically, we report that the
phase describing a physiological system, if properly modeled
and extracted, is immune to the selected sensor for that
system, while other approaches might fail. In conclusion,
the proposed approach resolves the above-mentioned
scientific challenge. We expect its scientific impact on a
broad range of applications.},
Doi = {10.1101/2020.09.29.310417},
Key = {fds353257}
}
@article{fds353258,
Author = {Shen, C and Lin, Y-T and Wu, H-T},
Title = {Robust and scalable manifold learning via landmark diffusion
for long-term medical signal processing},
Year = {2020},
url = {http://dx.doi.org/10.1101/2020.05.31.126649},
Abstract = {Motivated by analyzing long-term physiological time series,
we design a robust and scalable spectral embedding
algorithm, coined the algorithm RObust and Scalable
Embedding via LANdmark Diffusion (ROSE-LAND). The key is
designing a diffusion process on the dataset, where the
diffusion is forced to interchange on a small subset called
the landmark set . In addition to demonstrating its
application to spectral clustering and image segmentation,
the algorithm is applied to study the long-term arterial
blood pressure waveform dynamics during a liver transplant
operation lasting for 12 hours long.},
Doi = {10.1101/2020.05.31.126649},
Key = {fds353258}
}
@article{fds347178,
Author = {Su, P-C and Miller, S and Idriss, S and Barker, P and Wu,
H-T},
Title = {Recovery of the fetal electrocardiogram for morphological
analysis from two trans-abdominal channels via optimal
shrinkage.},
Journal = {Physiol Meas},
Volume = {40},
Number = {11},
Pages = {115005},
Year = {2019},
Month = {December},
url = {http://dx.doi.org/10.1088/1361-6579/ab4b13},
Abstract = {OBJECTIVE: We propose a novel algorithm to recover fetal
electrocardiogram (ECG) for both the fetal heart rate
analysis and morphological analysis of its waveform from two
or three trans-abdominal maternal ECG channels. APPROACH: We
design an algorithm based on the optimal-shrinkage under the
wave-shape manifold model. For the fetal heart rate
analysis, the algorithm is evaluated on publicly available
database, 2013 PhyioNet/Computing in Cardiology Challenge,
set A (CinC2013). For the morphological analysis, we analyze
CinC2013 and another publicly available database,
non-invasive fetal ECG arrhythmia database (nifeadb), and
propose to simulate semi-real databases by mixing the
MIT-BIH normal sinus rhythm database and MITDB arrhythmia
database. MAIN RESULTS: For the fetal R peak detection, the
proposed algorithm outperforms all algorithms under
comparison. For the morphological analysis, the algorithm
provides an encouraging result in recovery of the fetal ECG
waveform, including PR, QT and ST intervals, even when the
fetus has arrhythmia, both in real and simulated databases.
SIGNIFICANCE: To the best of our knowledge, this is the
first work focusing on recovering the fetal ECG for
morphological analysis from two or three channels with an
algorithm potentially applicable for continuous fetal
electrocardiographic monitoring, which creates the potential
for long term monitoring purpose.},
Doi = {10.1088/1361-6579/ab4b13},
Key = {fds347178}
}
@article{fds348787,
Author = {Thai, DH and Wu, HT and Dunson, DB},
Title = {Locally convex kernel mixtures: Bayesian subspace
learning},
Journal = {Proceedings - 18th IEEE International Conference on Machine
Learning and Applications, ICMLA 2019},
Pages = {272-275},
Year = {2019},
Month = {December},
ISBN = {9781728145495},
url = {http://dx.doi.org/10.1109/ICMLA.2019.00051},
Abstract = {Kernel mixture models are routinely used for density
estimation. However, in multivariate settings, issues arise
in efficiently approximating lower-dimensional structure in
the data. For example, it is common to suppose that the
density is concentrated near a lower-dimensional non-linear
subspace or manifold. Typical kernels used to locally
approximate such subspaces are inflexible, so that a large
number of components are often needed. We propose a novel
class of LOcally COnvex (LOCO) kernels that are flexible in
adapting to nonlinear local structure. LOCO kernels are
induced by introducing random knots within local
neighborhoods, and generating data as a random convex
combination of these knots with adaptive weights and an
additive noise. For identifiability, we constrain all
observations from a particular component to have the same
mean. For Bayesian inference subject to this constraint, we
develop a hybrid Gibbs sampler and optimization algorithm
that incorporates a Lagrange multiplier within a splitting
method. The resulting LOCO algorithm is shown to
dramatically outperform typical Gaussian mixture models in
challenging examples.},
Doi = {10.1109/ICMLA.2019.00051},
Key = {fds348787}
}
@article{fds333710,
Author = {Talmon, R and Wu, HT},
Title = {Latent common manifold learning with alternating diffusion:
Analysis and applications},
Journal = {Applied and Computational Harmonic Analysis},
Volume = {47},
Number = {3},
Pages = {848-892},
Publisher = {Elsevier BV},
Year = {2019},
Month = {November},
url = {http://dx.doi.org/10.1016/j.acha.2017.12.006},
Abstract = {The analysis of data sets arising from multiple sensors has
drawn significant research attention over the years.
Traditional methods, including kernel-based methods, are
typically incapable of capturing nonlinear geometric
structures. We introduce a latent common manifold model
underlying multiple sensor observations for the purpose of
multimodal data fusion. A method based on alternating
diffusion is presented and analyzed; we provide theoretical
analysis of the method under the latent common manifold
model. To exemplify the power of the proposed framework,
experimental results in several applications are
reported.},
Doi = {10.1016/j.acha.2017.12.006},
Key = {fds333710}
}
@article{fds345811,
Author = {Korolj, A and Wu, H-T and Radisic, M},
Title = {A healthy dose of chaos: Using fractal frameworks for
engineering higher-fidelity biomedical systems.},
Journal = {Biomaterials},
Volume = {219},
Pages = {119363},
Year = {2019},
Month = {October},
url = {http://dx.doi.org/10.1016/j.biomaterials.2019.119363},
Abstract = {Optimal levels of chaos and fractality are distinctly
associated with physiological health and function in natural
systems. Chaos is a type of nonlinear dynamics that tends to
exhibit seemingly random structures, whereas fractality is a
measure of the extent of organization underlying such
structures. Growing bodies of work are demonstrating both
the importance of chaotic dynamics for proper function of
natural systems, as well as the suitability of fractal
mathematics for characterizing these systems. Here, we
review how measures of fractality that quantify the dose of
chaos may reflect the state of health across various
biological systems, including: brain, skeletal muscle, eyes
and vision, lungs, kidneys, tumours, cell regulation, skin
and wound repair, bone, vasculature, and the heart. We
compare how reports of either too little or too much chaos
and fractal complexity can be damaging to normal biological
function, and suggest that aiming for the healthy dose of
chaos may be an effective strategy for various biomedical
applications. We also discuss rising examples of the
implementation of fractal theory in designing novel
materials, biomedical devices, diagnostics, and clinical
therapies. Finally, we explain important mathematical
concepts of fractals and chaos, such as fractal dimension,
criticality, bifurcation, and iteration, and how they are
related to biology. Overall, we promote the effectiveness of
fractals in characterizing natural systems, and suggest
moving towards using fractal frameworks as a basis for the
research and development of better tools for the future of
biomedical engineering.},
Doi = {10.1016/j.biomaterials.2019.119363},
Key = {fds345811}
}
@article{fds348059,
Author = {Martinez, N and Bertran, M and Sapiro, G and Wu, HT},
Title = {Non-Contact Photoplethysmogram and Instantaneous Heart Rate
Estimation from Infrared Face Video},
Journal = {Proceedings - International Conference on Image Processing,
ICIP},
Volume = {2019-September},
Pages = {2020-2024},
Year = {2019},
Month = {September},
ISBN = {9781538662496},
url = {http://dx.doi.org/10.1109/ICIP.2019.8803109},
Abstract = {Extracting the instantaneous heart rate (iHR) from face
videos has been well studied in recent years. It is well
known that changes in skin color due to blood flow can be
captured using conventional cameras. One of the main
limitations of methods that rely on this principle is the
need of an illumination source. Moreover, they have to be
able to operate under different light conditions. One way to
avoid these constraints is using infrared cameras, allowing
the monitoring of iHR under low light conditions. In this
work, we present a simple, principled signal extraction
method that recovers the iHR from infrared face videos. We
tested the procedure on 7 participants, for whom we recorded
an electrocardiogram simultaneously with their infrared face
video. We checked that the recovered signal matched the
ground truth iHR, showing that infrared is a promising
alternative to conventional video imaging for heart rate
monitoring, especially in low light conditions. Code is
available at https://github.com/natalialmg/IR-iHR.},
Doi = {10.1109/ICIP.2019.8803109},
Key = {fds348059}
}
@article{fds361506,
Author = {Wang, YG and Womersley, RS and Wu, H-T and Yu, W-H},
Title = {Numerical computation of triangular complex spherical
designs with small mesh ratio},
Year = {2019},
Month = {July},
Abstract = {This paper provides triangular spherical designs for the
complex unit sphere $\Omega^d$ by exploiting the natural
correspondence between the complex unit sphere in $d$
dimensions and the real unit sphere in $2d-1$. The existence
of triangular and square complex spherical $t$-designs with
the optimal order number of points is established. A
variational characterization of triangular complex designs
is provided, with particular emphasis on numerical
computation of efficient triangular complex designs with
good geometric properties as measured by their mesh ratio.
We give numerical examples of triangular spherical
$t$-designs on complex unit spheres of dimension $d=2$ to
$6$.},
Key = {fds361506}
}
@article{fds340061,
Author = {Wu, H and Alagapan, S and Frohlich, F and Shin, HW},
Title = {Diffusion geometry approach to efficiently remove electrical
stimulation artifacts in intracranial electroencephalography},
Journal = {Journal of Neural Engineering},
Volume = {16},
Number = {3},
Pages = {036010},
Publisher = {IOP Publishing},
Year = {2019},
Month = {June},
url = {http://dx.doi.org/10.1088/1741-2552/aaf2ba},
Abstract = {<h4>Objective</h4>Cortical oscillations,
electrophysiological activity patterns, associated with
cognitive functions and impaired in many psychiatric
disorders can be observed in intracranial
electroencephalography (iEEG). Direct cortical stimulation
(DCS) may directly target these oscillations and may serve
as therapeutic approaches to restore functional impairments.
However, the presence of electrical stimulation artifacts in
neurophysiological data limits the analysis of the effects
of stimulation. Currently available methods suffer in
performance in the presence of nonstationarity inherent in
biological data.<h4>Approach</h4>Our algorithm, shape
adaptive nonlocal artifact removal (SANAR) is based on
unsupervised manifold learning. By estimating the Euclidean
median of k-nearest neighbors of each artifact in a nonlocal
fashion, we obtain a faithful representation of the artifact
which is then subtracted. This approach overcomes the
challenges presented by nonstationarity.<h4>Main
results</h4>SANAR is effective in removing stimulation
artifacts in the time domain while preserving the spectral
content of the endogenous neurophysiological signal. We
demonstrate the performance in a simulated dataset as well
as in human iEEG data. Using two quantitative measures, that
capture how much of information from endogenous activity is
retained, we demonstrate that SANAR's performance exceeds
that of one of the widely used approaches, independent
component analysis, in the time domain as well as the
frequency domain.<h4>Significance</h4>This approach allows
for the analysis of iEEG data, single channel or multiple
channels, during DCS, a crucial step in advancing our
understanding of the effects of periodic stimulation and
developing new therapies.},
Doi = {10.1088/1741-2552/aaf2ba},
Key = {fds340061}
}
@article{fds341877,
Author = {Lu, Y and Wu, HT and Malik, J},
Title = {Recycling cardiogenic artifacts in impedance
pneumography},
Journal = {Biomedical Signal Processing and Control},
Volume = {51},
Pages = {162-170},
Year = {2019},
Month = {May},
url = {http://dx.doi.org/10.1016/j.bspc.2019.02.027},
Abstract = {Purpose: Biomedical sensors often exhibit cardiogenic
artifacts which, while distorting the signal of interest,
carry useful hemodynamic information. We propose an
algorithm to remove and extract hemodynamic information from
these cardiogenic artifacts. Methods: We apply a nonlinear
time-frequency analysis technique, the de-shape
synchrosqueezing transform (dsSST), to adaptively isolate
the high- and low-frequency components of a single-channel
signal. We demonstrate this technique's effectiveness by
removing and deriving hemodynamic information from the
cardiogenic artifact in an impedance pneumography (IP).
Results: The instantaneous heart rate is extracted, and the
cardiac and respiratory signals are reconstructed.
Conclusions: The dsSST is suitable for generating useful
hemodynamic information from the cardiogenic artifact in a
single-channel IP. We propose that the usefulness of the
dsSST as a recycling tool extends to other biomedical
sensors exhibiting cardiogenic artifacts.},
Doi = {10.1016/j.bspc.2019.02.027},
Key = {fds341877}
}
@article{fds361349,
Author = {Sourisseau, M and Wu, H-T and Zhou, Z},
Title = {Asymptotic analysis of synchrosqueezing transform -- toward
statistical inference with nonlinear-type time-frequency
analysis},
Year = {2019},
Month = {April},
Abstract = {We provide a statistical analysis of a tool in
nonlinear-type time-frequency analysis, the synchrosqueezing
transform (SST), for both the null and non-null cases. The
intricate nonlinear interaction of different quantities in
SST is quantified by carefully analyzing relevant
multivariate complex Gaussian random variables.
Specifically, we provide the quotient distribution of
dependent and improper complex Gaussian random variables.
Then, a central limit theorem result for SST is established.
{As an example}, we provide a block bootstrap scheme based
on the established SST theory to test if a given time series
contains oscillatory components.},
Key = {fds361349}
}
@article{fds361350,
Author = {Gavish, M and Talmon, R and Su, P-C and Wu, H-T},
Title = {Optimal Recovery of Precision Matrix for Mahalanobis
Distance from High Dimensional Noisy Observations in
Manifold Learning},
Year = {2019},
Month = {April},
Abstract = {Motivated by establishing theoretical foundations for
various manifold learning algorithms, we study the problem
of Mahalanobis distance (MD), and the associated precision
matrix, estimation from high-dimensional noisy data. By
relying on recent transformative results in covariance
matrix estimation, we demonstrate the sensitivity of \MD~and
the associated precision matrix to measurement noise,
determining the exact asymptotic signal-to-noise ratio at
which MD fails, and quantifying its performance otherwise.
In addition, for an appropriate loss function, we propose an
asymptotically optimal shrinker, which is shown to be
beneficial over the classical implementation of the MD, both
analytically and in simulations. The result is extended to
the manifold setup, where the nonlinear interaction between
curvature and high-dimensional noise is taken care of. The
developed solution is applied to study a multiscale
reduction problem in the dynamical system
analysis.},
Key = {fds361350}
}
@article{fds342474,
Author = {Chen, H-Y and Pan, H-C and Chen, Y-C and Chen, Y-C and Lin, Y-H and Yang,
S-H and Chen, J-L and Wu, H-T},
Title = {Traditional Chinese medicine use is associated with lower
end-stage renal disease and mortality rates among patients
with diabetic nephropathy: a population-based cohort
study.},
Journal = {BMC complementary and alternative medicine},
Volume = {19},
Number = {1},
Pages = {81},
Year = {2019},
Month = {April},
url = {http://dx.doi.org/10.1186/s12906-019-2491-y},
Abstract = {<h4>Background</h4>Diabetic nephropathy (DN) is a common
complication of diabetes mellitus (DM) that imposes an
enormous burden on the healthcare system. Although some
studies show that traditional Chinese medicine (TCM)
treatments confer a protective effect on DN, the long-term
impact remains unclear. This study aims to examine end-stage
renal disease (ESRD) and mortality rates among TCM users
with DN.<h4>Methods</h4>A total of 125,490 patients with
incident DN patients from 2004 to 2006 were identified from
the National Health Insurance Research Database in Taiwan
and followed until 2012. The landmark method was applied to
avoid immortal time bias, and propensity score matching was
used to select 1:1 baseline characteristics-matched cohort.
The Kaplan-Meier method and competing-risk analysis were
used to assess mortality and ESRD rates separately.<h4>Results</h4>Among
all eligible subjects, about 60% of patients were classified
as TCM users (65,812 TCM users and 41,482 nonusers). After
1:1 matching, the outcomes of 68,882 patients were analyzed.
For the ESRD rate, the 8-year cumulative incidence was 14.5%
for TCM users [95% confidence interval (CI): 13.9-15.0] and
16.6% for nonusers (95% CI: 16.0-17.2). For the mortality
rate, the 8-year cumulative incidence was 33.8% for TCM
users (95% CI: 33.1-34.6) and 49.2% for nonusers (95% CI:
48.5-49.9). After adjusting for confounding covariates, the
cause-specific hazard ratio of ESRD was 0.81 (95% CI:
0.78-0.84), and the hazard ratio of mortality for TCM users
was 0.48 (95% CI: 0.47-0.50). The cumulative incidence of
mortality increased rapidly among TCM users with ESRD (56.8,
95% CI: 54.6-59.1) when compared with TCM users without ESRD
(30.1, 95% CI: 29.4-30.9). In addition, TCM users who used
TCM longer or initiated TCM treatments after being diagnosed
with DN were associated with a lower risk of mortality.
These results were consistent across sensitivity tests with
different definitions of TCM users and inverse probability
weighting of subjects.<h4>Conclusions</h4>The lower ESRD and
mortality rates among patients with incident DN correlates
with the use of TCM treatments. Further studies about
specific TCM modalities or medications for DN are still
needed.},
Doi = {10.1186/s12906-019-2491-y},
Key = {fds342474}
}
@article{fds335550,
Author = {Zhang, JT and Cheng, MY and Wu, HT and Zhou, B},
Title = {A new test for functional one-way ANOVA with applications to
ischemic heart screening},
Journal = {Computational Statistics and Data Analysis},
Volume = {132},
Pages = {3-17},
Publisher = {Elsevier BV},
Year = {2019},
Month = {April},
url = {http://dx.doi.org/10.1016/j.csda.2018.05.004},
Abstract = {Motivated by an ischemic heart screening problem, a new
global test for one-way ANOVA in functional data analysis is
studied. The test statistic is taken as the maximum of the
pointwise F-test statistic over the interval the functional
responses are observed. Nonparametric bootstrap, which is
applicable in more general situations and easier to
implement than parametric bootstrap, is employed to
approximate the null distribution and to obtain an
approximate critical value. Under mild conditions,
asymptotically our test has the correct level and is root-n
consistent in detecting local alternatives. Simulation
studies show that the proposed test outperforms several
existing tests in terms of both size control and power when
the correlation between observations at any two different
points is high or moderate, and it is comparable with the
competitors otherwise. Application to an ischemic heart
dataset suggests that resting electrocardiogram signals may
contain enough information for ischemic heart screening at
outpatient clinics, without the help of stress tests
required by the current standard procedure.},
Doi = {10.1016/j.csda.2018.05.004},
Key = {fds335550}
}
@article{fds335551,
Author = {Tan, C and Zhang, L and Wu, H-T},
Title = {A Novel Blaschke Unwinding Adaptive-Fourier-Decomposition-Based
Signal Compression Algorithm With Application on ECG
Signals.},
Journal = {IEEE journal of biomedical and health informatics},
Volume = {23},
Number = {2},
Pages = {672-682},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2019},
Month = {March},
url = {http://dx.doi.org/10.1109/jbhi.2018.2817192},
Abstract = {This paper presents a novel signal compression algorithm
based on the Blaschke unwinding adaptive Fourier
decomposition (AFD). The Blaschke unwinding AFD is a newly
developed signal decomposition theory. It utilizes the
Nevanlinna factorization and the maximal selection principle
in each decomposition step, and achieves a faster
convergence rate with higher fidelity. The proposed
compression algorithm is applied to the electrocardiogram
signal. To assess the performance of the proposed
compression algorithm, in addition to the generic assessment
criteria, we consider the less discussed criteria related to
the clinical needs-for the heart rate variability analysis
purpose, how accurate the R-peak information is preserved is
evaluated. The experiments are conducted on the MIT-BIH
arrhythmia benchmark database. The results show that the
proposed algorithm performs better than other
state-of-the-art approaches. Meanwhile, it also well
preserves the R-peak information.},
Doi = {10.1109/jbhi.2018.2817192},
Key = {fds335551}
}
@article{fds335552,
Author = {Katz, O and Talmon, R and Lo, YL and Wu, HT},
Title = {Alternating diffusion maps for multimodal data
fusion},
Journal = {Information Fusion},
Volume = {45},
Pages = {346-360},
Publisher = {Elsevier BV},
Year = {2019},
Month = {January},
url = {http://dx.doi.org/10.1016/j.inffus.2018.01.007},
Abstract = {The problem of information fusion from multiple data-sets
acquired by multimodal sensors has drawn significant
research attention over the years. In this paper, we focus
on a particular problem setting consisting of a physical
phenomenon or a system of interest observed by multiple
sensors. We assume that all sensors measure some aspects of
the system of interest with additional sensor-specific and
irrelevant components. Our goal is to recover the variables
relevant to the observed system and to filter out the
nuisance effects of the sensor-specific variables. We
propose an approach based on manifold learning, which is
particularly suitable for problems with multiple modalities,
since it aims to capture the intrinsic structure of the data
and relies on minimal prior model knowledge. Specifically,
we propose a nonlinear filtering scheme, which extracts the
hidden sources of variability captured by two or more
sensors, that are independent of the sensor-specific
components. In addition to presenting a theoretical
analysis, we demonstrate our technique on real measured data
for the purpose of sleep stage assessment based on multiple,
multimodal sensor measurements. We show that without prior
knowledge on the different modalities and on the measured
system, our method gives rise to a data-driven
representation that is well correlated with the underlying
sleep process and is robust to noise and sensor-specific
effects.},
Doi = {10.1016/j.inffus.2018.01.007},
Key = {fds335552}
}
@article{fds346396,
Author = {Shnitzer, T and Lederman, RR and Liu, GR and Talmon, R and Wu,
HT},
Title = {Diffusion operators for multimodal data analysis},
Volume = {20},
Pages = {1-39},
Year = {2019},
Month = {January},
url = {http://dx.doi.org/10.1016/bs.hna.2019.07.008},
Abstract = {In this chapter, we present a Manifold Learning viewpoint on
the analysis of data arising from multiple modalities. We
assume that the high-dimensional multimodal data lie on
underlying low-dimensional manifolds and devise a new
data-driven representation that accommodates this inherent
structure. Based on diffusion geometry, we present three
composite operators, facilitating different aspects of
fusion of information from different modalities in different
settings. These operators are shown to recover the common
structures and the differences between modalities in terms
of their intrinsic geometry and allow for the construction
of data-driven representations which capture these
characteristics. The properties of these operators are
demonstrated in four applications: recovery of the common
variable in two camera views, shape analysis, foetal heart
rate identification and sleep dynamics assessment.},
Doi = {10.1016/bs.hna.2019.07.008},
Key = {fds346396}
}
@article{fds346397,
Author = {Lin, Y-T and Lo, Y-L and Lin, C-Y and Frasch, MG and Wu,
H-T},
Title = {Unexpected sawtooth artifact in beat-to-beat pulse transit
time measured from patient monitor data.},
Journal = {PloS one},
Volume = {14},
Number = {9},
Pages = {e0221319},
Year = {2019},
Month = {January},
url = {http://dx.doi.org/10.1371/journal.pone.0221319},
Abstract = {<h4>Object</h4>It is increasingly popular to collect as much
data as possible in the hospital setting from clinical
monitors for research purposes. However, in this setup the
data calibration issue is often not discussed and, rather,
implicitly assumed, while the clinical monitors might not be
designed for the data analysis purpose. We hypothesize that
this calibration issue for a secondary analysis may become
an important source of artifacts in patient monitor data. We
test an off-the-shelf integrated photoplethysmography (PPG)
and electrocardiogram (ECG) monitoring device for its
ability to yield a reliable pulse transit time (PTT)
signal.<h4>Approach</h4>This is a retrospective clinical
study using two databases: one containing 35 subjects who
underwent laparoscopic cholecystectomy, another containing
22 subjects who underwent spontaneous breathing test in the
intensive care unit. All data sets include recordings of PPG
and ECG using a commonly deployed patient monitor. We
calculated the PTT signal offline.<h4>Main results</h4>We
report a novel constant oscillatory pattern in the PTT
signal and identify this pattern as a sawtooth artifact. We
apply an approach based on the de-shape method to visualize,
quantify and validate this sawtooth artifact.<h4>Significance</h4>The
PPG and ECG signals not designed for the PTT evaluation may
contain unwanted artifacts. The PTT signal should be
calibrated before analysis to avoid erroneous interpretation
of its physiological meaning.},
Doi = {10.1371/journal.pone.0221319},
Key = {fds346397}
}
@article{fds354952,
Author = {Kao, SL and Lien, HW and Liu, TC and Wu, HT and Fang, TY and Wang, PC and Liu,
YW},
Title = {Meniere's disease prognosis by learning from
transient-evoked otoacoustic emission signals},
Journal = {Proceedings of the International Congress on
Acoustics},
Volume = {2019-September},
Pages = {6505-6512},
Year = {2019},
Month = {January},
ISBN = {9783939296157},
url = {http://dx.doi.org/10.18154/RWTH-CONV-239245},
Abstract = {Accurate prognosis of Meniere's disease (MD) is difficult.
The aim of this study is to treat it as a machine-learning
problem through the analysis of transient-evoked (TE)
otoacoustic emission (OAE) data obtained from MD patients.
Thirty-three patients who received treatment were recruited,
and their distortion-product (DP) OAE, TEOAE, as well as
pure-tone audiograms were taken longitudinally up to 6
months after being diagnosed with MD. By hindsight, the
patients were separated into two groups: those whose outer
hair cell (OHC) functions eventually recovered, and those
that did not. TEOAE signals between 2.5-20 ms were
dimension-reduced via principal component analysis, and
binary classification was performed via the support vector
machine. Through cross-validation, we demonstrate that the
accuracy of prognosis can reach >80% based on data obtained
at the first visit. Further analysis also shows that the
TEOAE group delay at 1k and 2k Hz tend to be longer for the
group of ears that eventually recovered their OHC functions.
The group delay can further be compared between the
MD-affected ear and the opposite ear. The present results
suggest that TEOAE signals provide abundant information for
the prognosis of MD and the information could be extracted
by applying machine-learning techniques.},
Doi = {10.18154/RWTH-CONV-239245},
Key = {fds354952}
}
@article{fds363680,
Author = {Shnitzer, T and Ben-Chen, M and Guibas, L and Talmon, R and Wu,
H-T},
Title = {Recovering Hidden Components in Multimodal Data with
Composite Diffusion Operators},
Journal = {SIAM Journal on Mathematics of Data Science},
Volume = {1},
Number = {3},
Pages = {588-616},
Publisher = {Society for Industrial & Applied Mathematics
(SIAM)},
Year = {2019},
Month = {January},
url = {http://dx.doi.org/10.1137/18m1218157},
Doi = {10.1137/18m1218157},
Key = {fds363680}
}
@article{fds375362,
Author = {Malik, J and Shen, C and Wu, HT and Wu, N},
Title = {CONNECTING DOTS: FROM LOCAL COVARIANCE TO EMPIRICAL
INTRINSIC GEOMETRY AND LOCALLY LINEAR EMBEDDING},
Journal = {Pure and Applied Analysis},
Volume = {1},
Number = {4},
Pages = {515-542},
Year = {2019},
Month = {January},
url = {http://dx.doi.org/10.2140/paa.2019.1.515},
Abstract = {Local covariance structure under the manifold setup has been
widely applied in the machine-learning community. Based on
the established theoretical results, we provide an extensive
study of two relevant manifold learning algorithms,
empirical intrinsic geometry (EIG) and locally linear
embedding (LLE) under the manifold setup. Particularly, we
show that without an accurate dimension estimation, the
geodesic distance estimation by EIG might be corrupted.
Furthermore, we show that by taking the local covariance
matrix into account, we can more accurately estimate the
local geodesic distance. When understanding LLE based on the
local covariance structure, its intimate relationship with
the curvature suggests a variation of LLE depending on the
“truncation scheme”. We provide a theoretical analysis
of the variation.},
Doi = {10.2140/paa.2019.1.515},
Key = {fds375362}
}
@article{fds337335,
Author = {Lin, CY and Wu, HT},
Title = {Embeddings of Riemannian manifolds with finite eigenvector
fields of connection Laplacian},
Journal = {Calculus of Variations and Partial Differential
Equations},
Volume = {57},
Number = {5},
Publisher = {Springer Nature America, Inc},
Year = {2018},
Month = {October},
url = {http://dx.doi.org/10.1007/s00526-018-1401-3},
Abstract = {We study the problem asking if one can embed manifolds into
finite dimensional Euclidean spaces by taking finite number
of eigenvector fields of the connection Laplacian. This
problem is essential for the dimension reduction problem in
manifold learning. In this paper, we provide a positive
answer to the problem. Specifically, we use eigenvector
fields to construct local coordinate charts with low
distortion, and show that the distortion constants depend
only on geometric properties of manifolds with metrics in
the little Hölder space c2,α. Next, we use the coordinate
charts to embed the entire manifold into a finite
dimensional Euclidean space. The proof of the results relies
on solving the elliptic system and providing estimates for
eigenvector fields and the heat kernel and their gradients.
We also provide approximation results for eigenvector field
under the c2,α perturbation.},
Doi = {10.1007/s00526-018-1401-3},
Key = {fds337335}
}
@article{fds340248,
Author = {Escalona-Vargas, D and Wu, H-T and Frasch, MG and Eswaran,
H},
Title = {A Comparison of Five Algorithms for Fetal
Magnetocardiography Signal Extraction.},
Journal = {Cardiovascular engineering and technology},
Volume = {9},
Number = {3},
Pages = {483-487},
Publisher = {Springer Nature},
Year = {2018},
Month = {September},
url = {http://dx.doi.org/10.1007/s13239-018-0351-4},
Abstract = {Fetal magnetocardiography (fMCG) provides accurate and
reliable measurements of electrophysiological events in the
fetal heart and is capable of studying fetuses with
congenital heart diseases. A variety of techniques exist to
extract the fMCG signal with the demand for non-invasively
obtained fetal cardiac information. To the best of our
knowledge, there is no comparative study published in the
field as to how the various extraction algorithms perform.
We perform a comparative study of the ability of five
methods to extract the fMCG using real biomagnetic signals,
two of those methods are applied to real fMCG data for the
first time. Biomagnetic signals were recorded and processed
with each of the five methods to obtain fMCG. The R peaks of
the fMCG traces were obtained via a peak-detection
algorithm. From whole recording for each method, the fetal
heart rate (FHR) was calculated and used to perform FHR
variability (FHRV) analysis. Additionally, we calculated
durations from the PQRST complex from time-averaged data
during sinus rhythm. The five methods recovered the fMCG
signals, but two of them were able to extract cleaner fMCG
and the morphology was observed from the continuous data.
The time-averaged data showed very similar morphologies
between methods, but two of them displayed a signal
amplitude reduction on the R-waves and T-waves. Values of
PQRST durations, FHR and FHRV were in the range of previous
fetal cardiac studies. We have compared five methods for
fMCG extraction and showed their ability to perform fMCG
analysis.},
Doi = {10.1007/s13239-018-0351-4},
Key = {fds340248}
}
@article{fds338042,
Author = {Malik, J and Lo, Y-L and Wu, H-T},
Title = {Sleep-wake classification via quantifying heart rate
variability by convolutional neural network.},
Journal = {Physiological measurement},
Volume = {39},
Number = {8},
Pages = {085004},
Publisher = {IOP Publishing},
Year = {2018},
Month = {August},
url = {http://dx.doi.org/10.1088/1361-6579/aad5a9},
Abstract = {<h4>Objective</h4>Fluctuations in heart rate are intimately
related to changes in the physiological state of the
organism. We exploit this relationship by classifying a
human participant's wake/sleep status using his
instantaneous heart rate (IHR) series.<h4>Approach</h4>We
use a convolutional neural network (CNN) to build features
from the IHR series extracted from a whole-night
electrocardiogram (ECG) and predict every 30 s whether the
participant is awake or asleep. Our training database
consists of 56 normal participants, and we consider three
different databases for validation; one is private, and two
are public with different races and apnea
severities.<h4>Main results</h4>On our private database of
27 participants, our accuracy, sensitivity, specificity, and
[Formula: see text] values for predicting the wake stage are
[Formula: see text], 52.4%, 89.4%, and 0.83, respectively.
Validation performance is similar on our two public
databases. When we use the photoplethysmography instead of
the ECG to obtain the IHR series, the performance is also
comparable. A robustness check is carried out to confirm the
obtained performance statistics.<h4>Significance</h4>This
result advocates for an effective and scalable method for
recognizing changes in physiological state using
non-invasive heart rate monitoring. The CNN model adaptively
quantifies IHR fluctuation as well as its location in time
and is suitable for differentiating between the wake and
sleep stages.},
Doi = {10.1088/1361-6579/aad5a9},
Key = {fds338042}
}
@article{fds346591,
Author = {Lin, Y and Wu, H and Yang, Z and Lin, Q},
Title = {Erratum: Validation of the Name Paraphlomis hispida
(Lamiaceae) (Novon (2017) 25 (436-437) DOI:
10.3417/D-16-00022)},
Journal = {Novon},
Volume = {26},
Number = {2},
Pages = {256},
Year = {2018},
Month = {August},
url = {http://dx.doi.org/10.3417/2018296},
Abstract = {The name Paraphlomis hispida C. Y. Wu (Lamiaceae) was not
initially validly published due to the author's failure to
select a single type. Lin et al. published a validation of
this name in Novon, Vol. 25 (2017: 436-437), designating C.
W. Wang 83872 (PE) as the holotype. It has since come to
those authors- attention that two earlier authors, Xiang and
Peng, in Bangladesh J. Pl. Taxon. 15: 73-74, had already
validated that name in 2008, likewise ascribing it solely to
C. Y. Wu but designating C. W. Wang 85447 (KUN) as the
holotype. The intended validation by Lin et al. is therefore
superfluous, and the specimen selected by Xiang and Peng is
the correct type.},
Doi = {10.3417/2018296},
Key = {fds346591}
}
@article{fds335547,
Author = {Wu, HT and Wu, JC and Huang, PC and Lin, TY and Wang, TY and Huang, YH and Lo,
YL},
Title = {Phenotype-based and self-learning inter-individual sleep
apnea screening with a level IV-like monitoring
system},
Journal = {Frontiers in Physiology},
Volume = {9},
Number = {JUL},
Publisher = {FRONTIERS MEDIA SA},
Year = {2018},
Month = {July},
url = {http://dx.doi.org/10.3389/fphys.2018.00723},
Abstract = {Purpose: We propose a phenotype-based artificial
intelligence system that can self-learn and is accurate for
screening purposes and test it on a Level IV-like monitoring
system. Methods: Based on the physiological knowledge, we
hypothesize that the phenotype information will allow us to
find subjects from a well-annotated database that share
similar sleep apnea patterns. Therefore, for a new-arriving
subject, we can establish a prediction model from the
existing database that is adaptive to the subject. We test
the proposed algorithm on a database consisting of 62
subjects with the signals recorded from a Level IV-like
wearable device measuring the thoracic and abdominal
movements and the SpO2. Results: With the
leave-one-subject-out cross validation, the accuracy of the
proposed algorithm to screen subjects with an apnea-hypopnea
index greater or equal to 15 is 93.6%, the positive
likelihood ratio is 6.8, and the negative likelihood ratio
is 0.03. Conclusion: The results confirm the hypothesis and
show that the proposed algorithm has potential to screen
patients with SAS.},
Doi = {10.3389/fphys.2018.00723},
Key = {fds335547}
}
@article{fds337015,
Author = {Wu, H-T and Liu, Y-W},
Title = {Analyzing transient-evoked otoacoustic emissions by
concentration of frequency and time.},
Journal = {The Journal of the Acoustical Society of
America},
Volume = {144},
Number = {1},
Pages = {448},
Publisher = {Acoustical Society of America (ASA)},
Year = {2018},
Month = {July},
url = {http://dx.doi.org/10.1121/1.5047749},
Abstract = {The linear part of transient evoked otoacoustic emission
(TEOAE) is thought to be generated via coherent reflection
near the characteristic place of constituent wave
components. Because of the tonotopic organization of the
cochlea, high frequency emissions return earlier than low
frequencies; however, due to the random nature of coherent
reflection, the instantaneous frequency (IF) and amplitude
envelope of TEOAEs both fluctuate. Multiple reflection
components and synchronized spontaneous emissions can
further make it difficult to extract the IF by linear
transforms. This paper proposes to model TEOAEs as a sum of
intrinsic mode-type functions and analyze it by a
nonlinear-type time-frequency (T-F) analysis technique
called concentration of frequency and time (ConceFT). When
tested with synthetic otoacoustic emission signals with
possibly multiple oscillatory components, the present method
is able to produce clearly visualized traces of individual
components on the T-F plane. Further, when the signal is
noisy, the proposed method is compared with existing linear
and bilinear methods in its accuracy for estimating the
fluctuating IF. Results suggest that ConceFT outperforms the
best of these methods in terms of optimal transport
distance, reducing the error by 10% to 21% when the signal
to noise ratio is 10 dB or below.},
Doi = {10.1121/1.5047749},
Key = {fds337015}
}
@article{fds354213,
Author = {Lin, CY and Minasian, A and Qi, XJ and Wu, HT},
Title = {Manifold Learning via the Principle Bundle
Approach},
Journal = {Frontiers in Applied Mathematics and Statistics},
Volume = {4},
Year = {2018},
Month = {June},
url = {http://dx.doi.org/10.3389/fams.2018.00021},
Abstract = {In this paper, we propose a novel principal bundle model and
apply it to the image denoising problem. This model is based
on the fact that the patch manifold admits canonical groups
actions such as rotation. We introduce an image denoising
algorithm, called the diffusive vector non-local Euclidean
median (dvNLEM), by combining the traditional nonlocal
Euclidean median (NLEM), the rotational structure in the
patch space, and the diffusion distance. A theoretical
analysis of dvNLEM, as well as the traditional nonlocal
Euclidean median (NLEM), is provided to explain why these
algorithms work. In particular, we show how accurate we
could obtain the true neighbors associated with the
rotationally invariant distance (RID) and Euclidean distance
in the patch space when noise exists, and how we could apply
the diffusion geometry to stabilize the selected metric. The
dvNLEM is applied to an image database of 1,361 images and a
comparison with the NLEM is provided. Different image
quality assessments based on the error-sensitivity or the
human visual system are applied to evaluate the
performance.},
Doi = {10.3389/fams.2018.00021},
Key = {fds354213}
}
@article{fds335548,
Author = {Liu, TC and Wu, HT and Chen, YH and Fang, TY and Wang, PC and Liu,
YW},
Title = {Analysis of click-evoked otoacoustic emissions by
concentration of frequency and time: Preliminary results
from normal hearing and Ménière's disease
ears},
Journal = {AIP Conference Proceedings},
Volume = {1965},
Publisher = {Author(s)},
Year = {2018},
Month = {May},
ISBN = {9780735416703},
url = {http://dx.doi.org/10.1063/1.5038538},
Abstract = {The presence of click-evoked (CE) otoacoustic emissions
(OAEs) has been clinically accepted as an indicator of
normal cochlear processing of sounds. For treatment and
diagnostic purposes, however, clinicians do not typically
pay attention to the detailed spectrum and waveform of
CEOAEs. A possible reason is due to the lack of noise-robust
signal processing tools to estimate physiologically
meaningful time-frequency properties of CEOAEs, such as the
latency of spectral components. In this on-going study, we
applied a modern tool called concentration of frequency and
time (ConceFT, [1]) to analyze CEOAE waveforms. Randomly
combined orthogonal functions are used as windowing
functions for time-frequency analysis. The resulting
spectrograms are subject to nonlinear time-frequency
reassignment so as to enhance the concentration of
time-varying sinusoidal components. The results after
reassignment could be further averaged across the random
choice of windows. CEOAE waveforms are acquired by a linear
averaging paradigm, and longitudinal data are currently
being collected from patients with Ménière's disease (MD)
and a control group of normal hearing subjects. When CEOAE
is present, the ConceFT plots show traces of decreasing but
fluctuating instantaneous frequency against time. For
comparison purposes, same processing methods are also
applied to analyze CEOAE data from cochlear mechanics
simulation.},
Doi = {10.1063/1.5038538},
Key = {fds335548}
}
@article{fds335549,
Author = {Wu, H-T and Soliman, EZ},
Title = {A new approach for analysis of heart rate variability and QT
variability in long-term ECG recording.},
Journal = {Biomedical engineering online},
Volume = {17},
Number = {1},
Pages = {54},
Year = {2018},
Month = {May},
url = {http://dx.doi.org/10.1186/s12938-018-0490-8},
Abstract = {<h4>Background and purpose</h4>With the emergence of
long-term electrocardiogram (ECG) recordings that extend
several days beyond the typical 24-48 h, the development of
new tools to measure heart rate variability (HRV) and QT
variability is needed to utilize the full potential of such
extra-long-term ECG recordings.<h4>Methods</h4>In this
report, we propose a new nonlinear time-frequency analysis
approach, the concentration of frequency and time (ConceFT),
to study the HRV QT variability from extra-long-term ECG
recordings. This approach is a generalization of Short Time
Fourier Transform and Continuous Wavelet Transform
approaches.<h4>Results</h4>As proof of concept, we used
14-day ECG recordings to show that the ConceFT provides a
sharpened and stabilized spectrogram by taking the phase
information of the time series and the multitaper technique
into account.<h4>Conclusion</h4>The ConceFT has the
potential to provide a sharpened and stabilized spectrogram
for the heart rate variability and QT variability in 14-day
ECG recordings.},
Doi = {10.1186/s12938-018-0490-8},
Key = {fds335549}
}
@article{fds339912,
Author = {Wu, JC and Wang, CW and Huang, YH and Wu, HT and Huang, PC and Lo,
YL},
Title = {A Portable Monitoring System with Automatic Event Detection
for Sleep Apnea Level-IV Evaluation},
Journal = {Proceedings - IEEE International Symposium on Circuits and
Systems},
Volume = {2018-May},
Year = {2018},
Month = {April},
ISBN = {9781538648810},
url = {http://dx.doi.org/10.1109/ISCAS.2018.8351221},
Abstract = {To meet the demands on a comfortable screening, or even
diagnostic, equipment without interfering with the sleep,
this study develops a level IV portable system, equipped
with two tri-axial accelerometers (TAA) measuring the
thoracic and abdominal respiratory efforts, and one oximeter
measuring the oxygen saturation (SpO2), to identify
obstructive sleep apnea (OSA), central sleep apnea (CSA),
and hypopnea (HYP) events. The prototype integrates all the
hardware and software for physiological information
extraction. In addition, an automatic event detection
algorithm is proposed to reduce the labor-intensive work on
scoring the events. Based on 63 subjects, with 80% data for
training and 20% for validation, the classification accuracy
of the apnea hypopnea-index (AHI) is 84.13%. The results
indicate that the proposed algorithm has great potential to
classify the severity of patients in clinical examinations
for both the screening and the homecare purposes.},
Doi = {10.1109/ISCAS.2018.8351221},
Key = {fds339912}
}
@article{fds340355,
Author = {Lin, CY and Su, L and Wu, HT},
Title = {Wave-Shape Function Analysis: When Cepstrum Meets
Time–Frequency Analysis},
Journal = {Journal of Fourier Analysis and Applications},
Volume = {24},
Number = {2},
Pages = {451-505},
Publisher = {Springer Nature},
Year = {2018},
Month = {April},
url = {http://dx.doi.org/10.1007/s00041-017-9523-0},
Abstract = {We propose to combine cepstrum and nonlinear
time–frequency (TF) analysis to study multiple component
oscillatory signals with time-varying frequency and
amplitude and with time-varying non-sinusoidal oscillatory
pattern. The concept of cepstrum is applied to eliminate the
wave-shape function influence on the TF analysis, and we
propose a new algorithm, named de-shape synchrosqueezing
transform (de-shape SST). The mathematical model, adaptive
non-harmonic model, is introduced and the de-shape SST
algorithm is theoretically analyzed. In addition to
simulated signals, several different physiological, musical
and biological signals are analyzed to illustrate the
proposed algorithm.},
Doi = {10.1007/s00041-017-9523-0},
Key = {fds340355}
}
@article{fds332750,
Author = {Shen, C and Frasch, MG and Wu, HT and Herry, CL and Cao, M and Desrochers,
A and Fecteau, G and Burns, P},
Title = {Non-invasive acquisition of fetal ECG from the maternal
xyphoid process: a feasibility study in pregnant sheep and a
call for open data sets.},
Journal = {Physiological measurement},
Volume = {39},
Number = {3},
Pages = {035005},
Year = {2018},
Month = {March},
url = {http://dx.doi.org/10.1088/1361-6579/aaaaa4},
Abstract = {<h4>Objective</h4>The utility of fetal heart rate (FHR)
monitoring can only be achieved with an acquisition sampling
rate that preserves the underlying physiological information
on the millisecond time scale (1000 Hz rather than 4 Hz).
For such acquisition, fetal ECG (fECG) is required, rather
than the ultrasound to derive FHR. We tested one recently
developed algorithm, SAVER, and two widely applied
algorithms to extract fECG from a single-channel maternal
ECG signal recorded over the xyphoid process rather than the
routine abdominal signal.<h4>Approach</h4>At 126dG, ECG was
attached to near-term ewe and fetal shoulders, manubrium and
xyphoid processes (n = 12). fECG served as the
ground-truth to which the fetal ECG signal extracted from
the simultaneously-acquired maternal ECG was compared. All
fetuses were in good health during surgery (pH
7.29 ± 0.03, pO<sub>2</sub> 33.2 ± 8.4,
pCO<sub>2</sub> 56.0 ± 7.8, O<sub>2</sub>Sat
78.3 ± 7.6, lactate 2.8 ± 0.6,
BE -0.3 ± 2.4).<h4>Main result</h4>In all
animals, single lead fECG extraction algorithm could not
extract fECG from the maternal ECG signal over the xyphoid
process with the F1 less than 50%.<h4>Significance</h4>The
applied fECG extraction algorithms might be unsuitable for
the maternal ECG signal over the xyphoid process, or the
latter does not contain strong enough fECG signal, although
the lead is near the mother's abdomen. Fetal sheep model is
widely used to mimic various fetal conditions, yet ECG
recordings in a public data set form are not available to
test the predictive ability of fECG and FHR. We are making
this data set openly available to other researchers to
foster non-invasive fECG acquisition in this animal
model.},
Doi = {10.1088/1361-6579/aaaaa4},
Key = {fds332750}
}
@article{fds338084,
Author = {Wu, H and Wu, N},
Title = {Think globally, fit locally under the Manifold Setup:
Asymptotic Analysis of Locally Linear Embedding},
Journal = {Annals of Statistics},
Volume = {46},
Number = {6B},
Pages = {3805-3837},
Publisher = {Institute of Mathematical Statistics},
Editor = {Hsin, T},
Year = {2018},
Month = {January},
url = {http://dx.doi.org/10.1214/17-AOS1676},
Abstract = {Since its introduction in 2000, Locally Linear Embedding
(LLE) has been widely applied in data science. We provide an
asymptotical analysis of LLE under the manifold setup. We
show that for a general manifold, asymptotically we may not
obtain the Laplace–Beltrami operator, and the result may
depend on nonuniform sampling unless a correct
regularization is chosen. We also derive the corresponding
kernel function, which indicates that LLE is not a Markov
process. A comparison with other commonly applied nonlinear
algorithms, particularly a diffusion map, is provided and
its relationship with locally linear regression is also
discussed.},
Doi = {10.1214/17-AOS1676},
Key = {fds338084}
}
@article{fds328822,
Author = {Kowalski, M and Meynard, A and Wu, HT},
Title = {Convex Optimization approach to signals with fast varying
instantaneous frequency},
Journal = {Applied and Computational Harmonic Analysis},
Volume = {44},
Number = {1},
Pages = {89-122},
Publisher = {Elsevier BV},
Year = {2018},
Month = {January},
url = {http://dx.doi.org/10.1016/j.acha.2016.03.008},
Abstract = {Motivated by the limitation of analyzing oscillatory signals
composed of multiple components with fast-varying
instantaneous frequency, we approach the time-frequency
analysis problem by optimization. Based on the proposed
adaptive harmonic model, the time-frequency representation
of a signal is obtained by directly minimizing a functional,
which involves few properties an “ideal time-frequency
representation” should satisfy, for example, the signal
reconstruction and concentrative time-frequency
representation. FISTA (Fast Iterative Shrinkage-Thresholding
Algorithm) is applied to achieve an efficient numerical
approximation of the functional. We coin the algorithm as
Time-frequency bY COnvex OptimizatioN (Tycoon). The
numerical results confirm the potential of the Tycoon
algorithm.},
Doi = {10.1016/j.acha.2016.03.008},
Key = {fds328822}
}
@article{fds329941,
Author = {Wu, H-K and Ko, Y-S and Lin, Y-S and Wu, H-T and Tsai, T-H and Chang,
H-H},
Title = {Corrigendum to "The correlation between pulse diagnosis and
constitution identification in traditional Chinese medicine"
[Complementary Ther. Med. 30 (2017) 107-112].},
Journal = {Complementary therapies in medicine},
Volume = {35},
Pages = {145},
Year = {2017},
Month = {December},
url = {http://dx.doi.org/10.1016/j.ctim.2017.09.004},
Doi = {10.1016/j.ctim.2017.09.004},
Key = {fds329941}
}
@article{fds330706,
Author = {Lin, YY and Wu, HT and Hsu, CA and Huang, PC and Huang, YH and Lo,
YL},
Title = {Sleep Apnea Detection Based on Thoracic and Abdominal
Movement Signals of Wearable Piezoelectric
Bands},
Journal = {IEEE Journal of Biomedical and Health Informatics},
Volume = {21},
Number = {6},
Pages = {1533-1545},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2017},
Month = {November},
url = {http://dx.doi.org/10.1109/JBHI.2016.2636778},
Abstract = {Physiologically, the thoracic (THO) and abdominal (ABD)
movement signals, captured using wearable piezoelectric
bands, provide information about various types of apnea,
including central sleep apnea (CSA) and obstructive sleep
apnea (OSA). However, the use of piezoelectric wearables in
detecting sleep apnea events has been seldom explored in the
literature. This study explored the possibility of
identifying sleep apnea events, including OSA and CSA, by
solely analyzing one or both the THO and ABD signals. An
adaptive nonharmonic model was introduced to model the THO
and ABD signals, which allows us to design features for
sleep apnea events. To confirm the suitability of the
extracted features, a support vector machine was applied to
classify three categories - normal and hypopnea, OSA, and
CSA. According to a database of 34 subjects, the overall
classification accuracies were on average 75.9%± 11.7% and
73.8%± 4.4%, respectively, based on the cross validation.
When the features determined from the THO and ABD signals
were combined, the overall classification accuracy became
81.8%± 9.4%. These features were applied for designing a
state machine for online apnea event detection. Two
event-by-event accuracy indexes, S and I, were proposed for
evaluating the performance of the state machine. For the
same database, the S index was 84.01%± 9.06% and the I
index was 77.21%± 19.01%. The results indicate the
considerable potential of applying the proposed algorithm to
clinical examinations for both screening and homecare
purposes.},
Doi = {10.1109/JBHI.2016.2636778},
Key = {fds330706}
}
@article{fds359935,
Author = {CHAO, YS and Wu, HT and Wu, CJ},
Title = {Feasibility of Classifying Life Stages and Searching for the
Determinants: Results from the Medical Expenditure Panel
Survey 1996–2011},
Journal = {Frontiers in Public Health},
Volume = {5},
Year = {2017},
Month = {October},
url = {http://dx.doi.org/10.3389/fpubh.2017.00247},
Abstract = {Background: Life stages are not clearly defined and
significant determinants for the identification of stages
are not discussed. This study aims to test a data-driven
approach to define stages and to identify the major
determinants. Methods: This study analyzed the data on the
Medical Expenditure Panel Survey interviewees from 1996 to
2011 in the United States. This study first selected
features with the Spearman’s correlation to remove
redundant variables and to increase computational
feasibility. The retained 430 variables were log
transformed, if applicable. Sixty-four nominal variables
were replaced with 164 binominal variables. This led to 525
variables that were available for principal component
analysis (PCA). Life stages were proposed to be periods of
ages with significantly different values of principal
components (PCs). Results: After retaining subjects followed
throughout the panels, 244,089 were eligible for PCA, and
the number of civilians was estimated to be 4.6 billion. The
age ranged from 0 to 90 years old (mean = 35.88, 95% CI =
35.67–36.09). The values of the first PC were not
significant from age of 6 to 13, 30 to 41, 46 to 60, and 76
to 90 years (adjusted p > 0.5), and the major determinants
were related to functional status, employment, and poverty.
Conclusion: Important stages and their major determinants,
including the status of functionality and cognition, income,
and marital status, can be identified. Identifying stages of
stability or transition will be important for research that
relies on a research population with similar characteristics
to draw samples for observation or intervention.
Contribution: This study sets an example of defining stages
of transition and stability across ages with social and
health data. Among all available variables, cognitive
limitations, income, and poverty are important determinants
of these stages.},
Doi = {10.3389/fpubh.2017.00247},
Key = {fds359935}
}
@article{fds328812,
Author = {Lin, T-Y and Fang, Y-F and Huang, S-H and Wang, T-Y and Kuo, C-H and Wu,
H-T and Kuo, H-P and Lo, Y-L},
Title = {Capnography monitoring the hypoventilation during the
induction of bronchoscopic sedation: A randomized controlled
trial.},
Journal = {Scientific reports},
Volume = {7},
Number = {1},
Pages = {8685},
Year = {2017},
Month = {August},
url = {http://dx.doi.org/10.1038/s41598-017-09082-8},
Abstract = {We hypothesize that capnography could detect hypoventilation
during induction of bronchoscopic sedation and starting
bronchoscopy following hypoventilation, may decrease
hypoxemia. Patients were randomized to: starting
bronchoscopy when hypoventilation (hypopnea, two successive
breaths of at least 50% reduction of the peak wave compared
to baseline or apnea, no wave for 10 seconds) (Study
group, n = 55), or when the Observer Assessment of
Alertness and Sedation scale (OAAS) was less than 4 (Control
group, n = 59). Propofol infusion was titrated to
maintain stable vital signs and sedative levels. The
hypoventilation during induction in the control group and
the sedative outcome were recorded. The patient
characteristics and procedures performed were similar.
Hypoventilation was observed in 74.6% of the patients before
achieving OAAS < 4 in the control group. Apnea occurred
more than hypopnea (p < 0.0001). Hypoventilation
preceded OAAS < 4 by 96.5 ± 88.1 seconds. In the
study group, the induction time was shorter (p = 0.03)
and subjects with any two events of hypoxemia during
sedation, maintenance or recovery were less than the control
group (1.8 vs. 18.6%, p < 0.01). Patient tolerance,
wakefulness during sedation, and cooperation were similar in
both groups. Significant hypoventilation occurred during the
induction and start bronchoscopy following hypoventilation
may decrease hypoxemia without compromising patient
tolerance.},
Doi = {10.1038/s41598-017-09082-8},
Key = {fds328812}
}
@article{fds329940,
Author = {Chao, Y-S and Wu, H-T and Scutari, M and Chen, T-S and Wu, C-J and Durand,
M and Boivin, A},
Title = {A network perspective on patient experiences and health
status: the Medical Expenditure Panel Survey 2004 to
2011.},
Journal = {BMC health services research},
Volume = {17},
Number = {1},
Pages = {579},
Year = {2017},
Month = {August},
url = {http://dx.doi.org/10.1186/s12913-017-2496-5},
Abstract = {<h4>Background</h4>There is a growing emphasis on the need
to engage patients in order to improve the quality of health
care and improve health outcomes. However, we are still
lacking a comprehensive understanding on how different
measures of patient experiences interact with one another or
relate to health status. This study takes a network
perspective to 1) study the associations between patient
characteristics and patient experience in health care and 2)
identify factors that could be prioritized to improve health
status.<h4>Methods</h4>This study uses data from the
two-year panels from the Medical Expenditure Panel Survey
(MEPS) initiated between 2004 and 2011 in the United States.
The 88 variables regarding patient health and experience
with health care were identified through the MEPS
documentation. Sex, age, race/ethnicity, and years of
education were also included for analysis. The bnlearn
package within R (v3.20) was used to 1) identify the
structure of the network of variables, 2) assess the model
fit of candidate algorithms, 3) cross-validate the network,
and 4) fit conditional probabilities with the given
structure.<h4>Results</h4>There were 51,023 MEPS
interviewees aged 18 to 85 years (mean = 44, 95%
CI = 43.9 to 44.2), with years of education ranging from 1
to 19 (mean = 7.4, 95% CI = 7.40 to 7.46). Among all,
55% and 74% were female and white, respectively. There were
nine networks identified and 17 variables not linked to
others, including death in the second years, sex, entry
years to the MEPS, and relations of proxies. The health
status in the second years was directly linked to that in
the first years. The health care ratings were associated
with how often professionals listened to them and whether
professionals' explanation was understandable.<h4>Conclusions</h4>It
is feasible to construct Bayesian networks with information
on patient characteristics and experiences in health care.
Network models help to identify significant predictors of
health care quality ratings. With temporal relationships
established, the structure of the variables can be
meaningful for health policy researchers, who search for one
or a few key priorities to initiate interventions or health
care quality improvement programs.},
Doi = {10.1186/s12913-017-2496-5},
Key = {fds329940}
}
@article{fds328814,
Author = {Georgiou, AS and Bello-Rivas, JM and Gear, CW and Wu, HT and Chiavazzo,
E and Kevrekidis, IG},
Title = {An exploration algorithm for stochastic simulators driven by
energy gradients},
Journal = {Entropy},
Volume = {19},
Number = {7},
Pages = {294-294},
Publisher = {MDPI AG},
Year = {2017},
Month = {July},
url = {http://dx.doi.org/10.3390/e19070294},
Abstract = {In recent work, we have illustrated the construction of an
exploration geometry on free energy surfaces: the adaptive
computer-assisted discovery of an approximate
low-dimensional manifold on which the effective dynamics of
the system evolves. Constructing such an exploration
geometry involves geometry-biased sampling (through both
appropriately-initialized unbiased molecular dynamics and
through restraining potentials) and, machine learning
techniques to organize the intrinsic geometry of the data
resulting from the sampling (in particular, diffusion maps,
possibly enhanced through the appropriate Mahalanobis-type
metric). In this contribution, we detail a method for
exploring the conformational space of a stochastic gradient
system whose effective free energy surface depends on a
smaller number of degrees of freedom than the dimension of
the phase space. Our approach comprises two steps. First, we
study the local geometry of the free energy landscape using
diffusion maps on samples computed through stochastic
dynamics. This allows us to automatically identify the
relevant coarse variables. Next, we use the information
garnered in the previous step to construct a new set of
initial conditions for subsequent trajectories. These
initial conditions are computed so as to explore the
accessible conformational space more efficiently than by
continuing the previous, unbiased simulations. We showcase
this method on a representative test system.},
Doi = {10.3390/e19070294},
Key = {fds328814}
}
@article{fds328813,
Author = {Malik, J and Reed, N and Wang, C-L and Wu, H-T},
Title = {Single-lead f-wave extraction using diffusion
geometry.},
Journal = {Physiological measurement},
Volume = {38},
Number = {7},
Pages = {1310-1334},
Year = {2017},
Month = {June},
url = {http://dx.doi.org/10.1088/1361-6579/aa707c},
Abstract = {<h4>Objective</h4>A novel single-lead f-wave extraction
algorithm based on the modern diffusion geometry data
analysis framework is proposed.<h4>Approach</h4>The
algorithm is essentially an averaged beat subtraction
algorithm, where the ventricular activity template is
estimated by combining a newly designed metric, the
'diffusion distance', and the non-local Euclidean median
based on the non-linear manifold setup. We coined the
algorithm [Formula: see text].<h4>Main results</h4>Two
simulation schemes are considered, and the new algorithm
[Formula: see text] outperforms traditional algorithms,
including the average beat subtraction, principal component
analysis, and adaptive singular value cancellation, in
different evaluation metrics with statistical
significance.<h4>Significance</h4>The clinical potential is
shown in the real Holter signal, and we introduce a new
score to evaluate the performance of the
algorithm.},
Doi = {10.1088/1361-6579/aa707c},
Key = {fds328813}
}
@article{fds328815,
Author = {Sheu, YL and Hsu, LY and Chou, PT and Wu, HT},
Title = {Entropy-based time-varying window width selection for
nonlinear-type time–frequency analysis},
Journal = {International Journal of Data Science and
Analytics},
Volume = {3},
Number = {4},
Pages = {231-245},
Publisher = {Springer Science and Business Media LLC},
Year = {2017},
Month = {June},
url = {http://dx.doi.org/10.1007/s41060-017-0053-2},
Abstract = {We propose a time-varying optimal window width (TVOWW) and
an adaptive optimal window width selection schemes to
optimize the performance of several nonlinear-type
time–frequency analyses, including the reassignment method
and its variations. A window rendering the most concentrated
distribution in the time–frequency representation is
regarded as the optimal window. The TVOWW selection scheme
is particularly useful for signals that comprise
fast-varying instantaneous frequencies and small spectral
gaps. To demonstrate the efficacy of the method, in addition
to analyzing synthetic signals, we study an atomic
time-varying dipole moment driven by two-color mid-infrared
laser fields in attosecond physics and near-threshold
harmonics of a hydrogen atom in the strong laser
field.},
Doi = {10.1007/s41060-017-0053-2},
Key = {fds328815}
}
@article{fds354214,
Author = {Su, L and Wu, HT},
Title = {Extract Fetal ECG from Single-Lead Abdominal ECG by De-Shape
Short Time Fourier Transform and Nonlocal
Median},
Journal = {Frontiers in Applied Mathematics and Statistics},
Volume = {3},
Year = {2017},
Month = {February},
url = {http://dx.doi.org/10.3389/fams.2017.00002},
Abstract = {The multiple fundamental frequency detection problem and the
source separation problem from a single-channel signal
containing multiple oscillatory components and a
nonstationary noise are both challenging tasks. To extract
the fetal electrocardiogram (ECG) from a single-lead
maternal abdominal ECG, we need to solve both challenges. We
propose a novel method to extract the fetal ECG from a
single-lead maternal abdominal ECG, without any additional
measurement. The algorithm is composed of three components.
First, the maternal and fetal heart rates are estimated by
the de-shape short time Fourier transform (STFT), which is a
recently proposed nonlinear time-frequency analysis
technique. The beat tracking technique is the second
component which is applied to accurately obtain the maternal
and fetal R peaks. The third component consists of
establishing the maternal and fetal ECG waveforms by the
nonlocal median. The algorithm is tested on two real
databases with the annotation provided by experts (adfecgdb
database and CinC2013 database) and a simulated database
(fecgsym), and provides the state-of-the-art results. We
conclude that with the proposed algorithm, the fetal ECG
waveform and the fetal heart rate could be accurately
obtained from the single-lead maternal abdominal
ECG.},
Doi = {10.3389/fams.2017.00002},
Key = {fds354214}
}
@article{fds328817,
Author = {Herry, CL and Frasch, M and Seely, AJ and Wu, H-T},
Title = {Heart beat classification from single-lead ECG using the
synchrosqueezing transform.},
Journal = {Physiological measurement},
Volume = {38},
Number = {2},
Pages = {171-187},
Year = {2017},
Month = {February},
url = {http://dx.doi.org/10.1088/1361-6579/aa5070},
Abstract = {The processing of ECG signal provides a wealth of
information on cardiac function and overall cardiovascular
health. While multi-lead ECG recordings are often necessary
for a proper assessment of cardiac rhythms, they are not
always available or practical, for example in fetal ECG
applications. Moreover, a wide range of small non-obtrusive
single-lead ECG ambulatory monitoring devices are now
available, from which heart rate variability (HRV) and other
health-related metrics are derived. Proper beat detection
and classification of abnormal rhythms is important for
reliable HRV assessment and can be challenging in
single-lead ECG monitoring devices. In this manuscript, we
modelled the heart rate signal as an adaptive non-harmonic
model and used the newly developed synchrosqueezing
transform (SST) to characterize ECG patterns. We show how
the proposed model can be used to enhance heart beat
detection and classification between normal and abnormal
rhythms. In particular, using the Massachusetts Institute of
Technology-Beth Israel Hospital (MIT-BIH) arrhythmia
database and the Association for the Advancement of Medical
Instrumentation (AAMI) beat classes, we trained and
validated a support vector machine (SVM) classifier on a
portion of the annotated beat database using the SST-derived
instantaneous phase, the R-peak amplitudes and R-peak to
R-peak interval durations, based on a single ECG lead. We
obtained sentivities and positive predictive values
comparable to other published algorithms using multiple
leads and many more features.},
Doi = {10.1088/1361-6579/aa5070},
Key = {fds328817}
}
@article{fds329944,
Author = {Wu, H-K and Ko, Y-S and Lin, Y-S and Wu, H-T and Tsai, T-H and Chang,
H-H},
Title = {The correlation between pulse diagnosis and constitution
identification in traditional Chinese medicine.},
Journal = {Complementary therapies in medicine},
Volume = {30},
Pages = {107-112},
Year = {2017},
Month = {February},
url = {http://dx.doi.org/10.1016/j.ctim.2016.12.005},
Abstract = {<h4>Objectives</h4>Our study aimed to correlate pulse wave
parameters such as augmentation index (AI) and heart rate
variability with traditional Chinese medicine (TCM)
constitution for evaluating health status.<h4>Design</h4>Out
of 177 subjects, 69 healthy subjects were enrolled in the
present study, and others were excluded because of
cardiovascular, liver, kidney, or other diseases. Each
subject was invited to complete pulse wave examination and
the Constitution in Chinese Medicine Questionnaire.
Independent Student's t-tests, Mann-Whitney tests, and
binary logistic regression analysis were used to analyse the
correlation between pulse wave parameters and TCM
constitution.<h4>Results</h4>Qi-deficient individuals had
higher AI (p=0.006) and lower diastolic blood pressure
(p=0.011); yang-deficient individuals had lower dP/dt max
(p=0.030), systolic blood pressure (p=0.020), and pulse
pressure (p=0.048); and damp-heat individuals had higher
subendocardial viability index (SEVI) scores (p=0.011). We
then categorized the phlegm dampness and yang-deficiency
individuals into the cold group and those with damp-heat and
yin-deficiency into the heat group. A comparison of the two
constitution groups showed higher AI in the cold group
(p=0.026). Binary logistic regression analysis demonstrated
that only AI was a determinant, as evidenced by the finding
that an increase of one unit in AI corresponded to an
increase of 5% in the odds ratio for individuals to have a
cold constitution (p=0.026).<h4>Conclusions</h4>Individuals
with qi-deficient and cold constitutions had higher AI and
lower SEVI, potentially reflecting an increase in arterial
stiffness. This study can provide a basis for further
investigation of the physiological indicators of TCM
constitutions in modern medicine.},
Doi = {10.1016/j.ctim.2016.12.005},
Key = {fds329944}
}
@article{fds328819,
Author = {Wu, HT},
Title = {Embedding Riemannian manifolds by the heat kernel of the
connection Laplacian},
Journal = {Advances in Mathematics},
Volume = {304},
Pages = {1055-1079},
Publisher = {Elsevier BV},
Year = {2017},
Month = {January},
url = {http://dx.doi.org/10.1016/j.aim.2016.05.023},
Abstract = {Given a class of closed Riemannian manifolds with prescribed
geometric conditions, we introduce an embedding of the
manifolds into ℓ2 based on the heat kernel of the
Connection Laplacian associated with the Levi-Civita
connection on the tangent bundle. As a result, we can
construct a distance in this class which leads to a
pre-compactness theorem on the class under
consideration.},
Doi = {10.1016/j.aim.2016.05.023},
Key = {fds328819}
}
@article{fds331926,
Author = {Wu, H and Steinerberger, S and Coifman, R},
Title = {Carrier frequencies, holomorphy and unwinding},
Journal = {SIAM Journal on Mathematical Analysis},
Volume = {49},
Number = {6},
Pages = {4838-4864},
Publisher = {Society for Industrial and Applied Mathematics},
Year = {2017},
Month = {January},
url = {http://dx.doi.org/10.1137/16M1081087},
Abstract = {We prove that functions of intrinsic-mode type (a classical
models for signals) behave essentially like holomorphic
functions: Adding a pure carrier frequency eint ensures that
the anti- holomorphic part is much smaller than the
holomorphic part lP-(f)||L ≪||-P+(f)||L . This enables us
to use techniques from complex analysis, in particular the
unwinding series. We study its stability and convergence
properties and show that the unwinding scries can provide a
high-resolution, noise- robust time-frequency
representation. 2 2},
Doi = {10.1137/16M1081087},
Key = {fds331926}
}
@article{fds328816,
Author = {Li, R and Frasch, MG and Wu, H-T},
Title = {Efficient Fetal-Maternal ECG Signal Separation from Two
Channel Maternal Abdominal ECG via Diffusion-Based Channel
Selection.},
Journal = {Frontiers in physiology},
Volume = {8},
Pages = {277},
Year = {2017},
Month = {January},
url = {http://dx.doi.org/10.3389/fphys.2017.00277},
Abstract = {There is a need for affordable, widely deployable
maternal-fetal ECG monitors to improve maternal and fetal
health during pregnancy and delivery. Based on the
diffusion-based channel selection, here we present the
mathematical formalism and clinical validation of an
algorithm capable of accurate separation of maternal and
fetal ECG from a two channel signal acquired over maternal
abdomen. The proposed algorithm is the first algorithm, to
the best of the authors' knowledge, focusing on the fetal
ECG analysis based on two channel maternal abdominal ECG
signal, and we apply it to two publicly available databases,
the PhysioNet non-invasive fECG database (adfecgdb) and the
2013 PhysioNet/Computing in Cardiology Challenge (CinC2013),
to validate the algorithm. The state-of-the-art results are
achieved when compared with other available algorithms.
Particularly, the <i>F</i><sub>1</sub> score for the R peak
detection achieves 99.3% for the adfecgdb and 87.93% for the
CinC2013, and the mean absolute error for the estimated R
peak locations is 4.53 ms for the adfecgdb and 6.21 ms for
the CinC2013. The method has the potential to be applied to
other fetal cardiogenic signals, including cardiac doppler
signals.},
Doi = {10.3389/fphys.2017.00277},
Key = {fds328816}
}
@article{fds329942,
Author = {Frasch, MG and Boylan, GB and Wu, H-T and Devane,
D},
Title = {Commentary: Computerised interpretation of fetal heart rate
during labour (INFANT): a randomised controlled
trial.},
Journal = {Frontiers in physiology},
Volume = {8},
Pages = {721},
Year = {2017},
Month = {January},
url = {http://dx.doi.org/10.3389/fphys.2017.00721},
Doi = {10.3389/fphys.2017.00721},
Key = {fds329942}
}
@article{fds329943,
Author = {Cicone, A and Wu, H-T},
Title = {How Nonlinear-Type Time-Frequency Analysis Can Help in
Sensing Instantaneous Heart Rate and Instantaneous
Respiratory Rate from Photoplethysmography in a Reliable
Way.},
Journal = {Frontiers in physiology},
Volume = {8},
Pages = {701},
Year = {2017},
Month = {January},
url = {http://dx.doi.org/10.3389/fphys.2017.00701},
Abstract = {Despite the population of the noninvasive, economic,
comfortable, and easy-to-install photoplethysmography (PPG),
it is still lacking a mathematically rigorous and stable
algorithm which is able to simultaneously extract from a
single-channel PPG signal the instantaneous heart rate (IHR)
and the instantaneous respiratory rate (IRR). In this paper,
a novel algorithm called deppG is provided to tackle this
challenge. deppG is composed of two theoretically solid
nonlinear-type time-frequency analyses techniques, the
de-shape short time Fourier transform and the
synchrosqueezing transform, which allows us to extract the
instantaneous physiological information from the PPG signal
in a reliable way. To test its performance, in addition to
validating the algorithm by a simulated signal and
discussing the meaning of "instantaneous," the algorithm is
applied to two publicly available batch databases, the
Capnobase and the ICASSP 2015 signal processing cup. The
former contains PPG signals relative to spontaneous or
controlled breathing in static patients, and the latter is
made up of PPG signals collected from subjects doing intense
physical activities. The accuracies of the estimated IHR and
IRR are compared with the ones obtained by other methods,
and represent the state-of-the-art in this field of
research. The results suggest the potential of deppG to
extract instantaneous physiological information from a
signal acquired from widely available wearable devices, even
when a subject carries out intense physical
activities.},
Doi = {10.3389/fphys.2017.00701},
Key = {fds329943}
}
@article{fds329945,
Author = {Liu, W-T and Wu, H-T and Juang, J-N and Wisniewski, A and Lee, H-C and Wu,
D and Lo, Y-L},
Title = {Prediction of the severity of obstructive sleep apnea by
anthropometric features via support vector
machine.},
Journal = {PloS one},
Volume = {12},
Number = {5},
Pages = {e0176991},
Year = {2017},
Month = {January},
url = {http://dx.doi.org/10.1371/journal.pone.0176991},
Abstract = {To develop an applicable prediction for obstructive sleep
apnea (OSA) is still a challenge in clinical practice. We
apply a modern machine learning method, the support vector
machine to establish a predicting model for the severity of
OSA. The support vector machine was applied to build up a
prediction model based on three anthropometric features
(neck circumference, waist circumference, and body mass
index) and age on the first database. The established model
was then valided independently on the second database. The
anthropometric features and age were combined to generate
powerful predictors for OSA. Following the common practice,
we predict if a subject has the apnea-hypopnea index greater
then 15 or not as well as 30 or not. Dividing by genders and
age, for the AHI threhosld 15 (respectively 30), the cross
validation and testing accuracy for the prediction were
85.3% and 76.7% (respectively 83.7% and 75.5%) in young
female, while the negative likelihood ratio for the AHI
threhosld 15 (respectively 30) for the cross validation and
testing were 0.2 and 0.32 (respectively 0.06 and 0.1) in
young female. The more accurate results with lower negative
likelihood ratio in the younger patients, especially the
female subgroup, reflect the potential of the proposed model
for the screening purpose and the importance of approaching
by different genders and the effects of aging.},
Doi = {10.1371/journal.pone.0176991},
Key = {fds329945}
}
@article{fds328818,
Author = {Lin, Y-T and Wu, H-T},
Title = {ConceFT for Time-Varying Heart Rate Variability Analysis as
a Measure of Noxious Stimulation During General
Anesthesia.},
Journal = {IEEE transactions on bio-medical engineering},
Volume = {64},
Number = {1},
Pages = {145-154},
Year = {2017},
Month = {January},
url = {http://dx.doi.org/10.1109/tbme.2016.2549048},
Abstract = {<h4>Objective</h4>Heart rate variability (HRV) offers a
noninvasive way to peek into the physiological status of the
human body. When this physiological status is dynamic,
traditional HRV indices calculated from power spectrum do
not resolve the dynamic situation due to the issue of
nonstationarity. Clinical anesthesia is a typically dynamic
situation that calls for time-varying HRV analysis.
Concentration of frequency and time (ConceFT) is a nonlinear
time-frequency (TF) analysis generalizing the multitaper
technique and the synchrosqueezing transform. The result is
a sharp TF representation capturing the dynamics inside HRV.
Companion indices of the commonly applied HRV indices,
including time-varying low-frequency power (tvLF),
time-varying high-frequency power, and time-varying low-high
ratio, are considered as measures of noxious
stimulation.<h4>Methods</h4>To evaluate the feasibility of
the proposed indices, we apply these indices to study two
different types of noxious stimulation, the endotracheal
intubation and surgical skin incision, under general
anesthesia. The performance was compared with traditional
HRV indices, the heart rate reading, and indices from
electroencephalography.<h4>Results</h4>The results indicate
that the tvLF index performs best and outperforms not only
the traditional HRV index, but also the commonly used heart
rate reading.<h4>Conclusion</h4>With the help of ConceFT,
the proposed HRV indices are potential to provide a better
quantification of the dynamic change of the autonomic nerve
system.<h4>Significance</h4>Our proposed scheme of
time-varying HRV analysis could contribute to the clinical
assessment of analgesia under general anesthesia.},
Doi = {10.1109/tbme.2016.2549048},
Key = {fds328818}
}
@article{fds346284,
Author = {Singer, A and Wu, HT},
Title = {Spectral convergence of the connection Laplacian from random
samples},
Journal = {Information and Inference},
Volume = {6},
Number = {1},
Pages = {58-123},
Year = {2017},
Month = {January},
url = {http://dx.doi.org/10.1093/imaiai/iaw016},
Abstract = {Spectral methods that are based on eigenvectors and
eigenvalues of discrete graph Laplacians, such as Diffusion
Maps and Laplacian Eigenmaps, are often used for manifold
learning and nonlinear dimensionality reduction. Itwas
previously shown by Belkin & Niyogi (2007, Convergence of
Laplacian eigenmaps, vol. 19. Proceedings of the 2006
Conference on Advances in Neural Information Processing
Systems. The MIT Press, p. 129.) that the eigenvectors and
eigenvalues of the graph Laplacian converge to the
eigenfunctions and eigenvalues of the Laplace-Beltrami
operator of the manifold in the limit of infinitely many
data points sampled independently from the uniform
distribution over the manifold. Recently, we introduced
Vector Diffusion Maps and showed that the connection
Laplacian of the tangent bundle of the manifold can be
approximated from random samples. In this article, we
present a unified framework for approximating other
connection Laplacians over the manifold by considering its
principle bundle structure. We prove that the eigenvectors
and eigenvalues of these Laplacians converge in the limit of
infinitely many independent random samples. We generalize
the spectral convergence results to the case where the data
points are sampled from a non-uniform distribution, and for
manifolds with and without boundary.},
Doi = {10.1093/imaiai/iaw016},
Key = {fds346284}
}
@article{fds329072,
Author = {Wu, C-H and Wang, T-D and Hsieh, C-H and Huang, S-H and Lin, J-W and Hsu,
S-C and Wu, H-T and Wu, Y-M and Liu, T-M},
Title = {Imaging Cytometry of Human Leukocytes with Third Harmonic
Generation Microscopy.},
Journal = {Scientific reports},
Volume = {6},
Number = {1},
Pages = {37210},
Year = {2016},
Month = {November},
url = {http://dx.doi.org/10.1038/srep37210},
Abstract = {Based on third-harmonic-generation (THG) microscopy and a
k-means clustering algorithm, we developed a label-free
imaging cytometry method to differentiate and determine the
types of human leukocytes. According to the size and average
intensity of cells in THG images, in a two-dimensional
scatter plot, the neutrophils, monocytes, and lymphocytes in
peripheral blood samples from healthy volunteers were
clustered into three differentiable groups. Using these
features in THG images, we could count the number of each of
the three leukocyte types both in vitro and in vivo. The THG
imaging-based counting results agreed well with conventional
blood count results. In the future, we believe that the
combination of this THG microscopy-based imaging cytometry
approach with advanced texture analysis of sub-cellular
features can differentiate and count more types of blood
cells with smaller quantities of blood.},
Doi = {10.1038/srep37210},
Key = {fds329072}
}
@article{fds328820,
Author = {Marchesini, S and Tu, YC and Wu, HT},
Title = {Alternating projection, ptychographic imaging and phase
synchronization},
Journal = {Applied and Computational Harmonic Analysis},
Volume = {41},
Number = {3},
Pages = {815-851},
Publisher = {Elsevier BV},
Year = {2016},
Month = {November},
url = {http://dx.doi.org/10.1016/j.acha.2015.06.005},
Abstract = {We demonstrate necessary and sufficient conditions of the
local convergence of the alternating projection algorithm to
a unique solution up to a global phase factor. Additionally,
for the ptychography imaging problem, we discuss phase
synchronization and graph connection Laplacian, and show how
to construct an accurate initial guess to accelerate
convergence speed to handle the big imaging data in the
coming new light source era.},
Doi = {10.1016/j.acha.2015.06.005},
Key = {fds328820}
}
@article{fds328301,
Author = {Wu, H-T and Lewis, GF and Davila, MI and Daubechies, I and Porges,
SW},
Title = {Optimizing Estimates of Instantaneous Heart Rate from Pulse
Wave Signals with the Synchrosqueezing Transform.},
Journal = {Methods of information in medicine},
Volume = {55},
Number = {5},
Pages = {463-472},
Year = {2016},
Month = {October},
url = {http://dx.doi.org/10.3414/me16-01-0026},
Abstract = {<h4>Background</h4>With recent advances in sensor and
computer technologies, the ability to monitor peripheral
pulse activity is no longer limited to the laboratory and
clinic. Now inexpensive sensors, which interface with
smartphones or other computer-based devices, are expanding
into the consumer market. When appropriate algorithms are
applied, these new technologies enable ambulatory monitoring
of dynamic physiological responses outside the clinic in a
variety of applications including monitoring fatigue,
health, workload, fitness, and rehabilitation. Several of
these applications rely upon measures derived from
peripheral pulse waves measured via contact or non-contact
photoplethysmography (PPG). As technologies move from
contact to non-contact PPG, there are new challenges. The
technology necessary to estimate average heart rate over a
few seconds from a noncontact PPG is available. However, a
technology to precisely measure instantaneous heat rate
(IHR) from non-contact sensors, on a beat-to-beat basis, is
more challenging.<h4>Objectives</h4>The objective of this
paper is to develop an algorithm with the ability to
accurately monitor IHR from peripheral pulse waves, which
provides an opportunity to measure the neural regulation of
the heart from the beat-to-beat heart rate pattern (i.e.,
heart rate variability).<h4>Methods</h4>The adaptive
harmonic model is applied to model the contact or
non-contact PPG signals, and a new methodology, the
Synchrosqueezing Transform (SST), is applied to extract IHR.
The body sway rhythm inherited in the non-contact PPG signal
is modeled and handled by the notion of wave-shape
function.<h4>Results</h4>The SST optimizes the extraction of
IHR from the PPG signals and the technique functions well
even during periods of poor signal to noise. We contrast the
contact and non-contact indices of PPG derived heart rate
with a criterion electrocardiogram (ECG). ECG and PPG
signals were monitored in 21 healthy subjects performing
tasks with different physical demands. The root mean square
error of IHR estimated by SST is significantly better than
commonly applied methods such as autoregressive (AR) method.
In the walking situation, while AR method fails, SST still
provides a reasonably good result.<h4>Conclusions</h4>The
SST processed PPG data provided an accurate estimate of the
ECG derived IHR and consistently performed better than
commonly applied methods such as autoregressive
method.},
Doi = {10.3414/me16-01-0026},
Key = {fds328301}
}
@article{fds328821,
Author = {Lin, Y-T and Flandrin, P and Wu, H-T},
Title = {When Interpolation-Induced Reflection Artifact Meets
Time-Frequency Analysis.},
Journal = {IEEE transactions on bio-medical engineering},
Volume = {63},
Number = {10},
Pages = {2133-2141},
Year = {2016},
Month = {October},
url = {http://dx.doi.org/10.1109/tbme.2015.2510580},
Abstract = {<h4>Objective</h4>While extracting the temporal dynamical
features based on the time-frequency analyses, like the
reassignment and synchrosqueezing transform, attracts more
and more interest in biomedical data analysis, we should be
careful about artifacts generated by interpolation schemes,
in particular when the sampling rate is not significantly
higher than the frequency of the oscillatory component we
are interested in.<h4>Methods</h4>We formulate the problem
called the reflection effect and provide a theoretical
justification of the statement. We also show examples in the
anesthetic depth analysis with clear but undesirable
artifacts.<h4>Results</h4>The artifact associated with the
reflection effect exists not only theoretically but
practically as well. Its influence is pronounced when we
apply the time-frequency analyses to extract the
time-varying dynamics hidden inside the signal.<h4>Conclusion</h4>We
have to carefully deal with the artifact associated with the
reflection effect by choosing a proper interpolation
scheme.},
Doi = {10.1109/tbme.2015.2510580},
Key = {fds328821}
}
@article{fds328302,
Author = {O'Neal, WT and Wang, YG and Wu, H-T and Zhang, Z-M and Li, Y and Tereshchenko, LG and Estes, EH and Daubechies, I and Soliman,
EZ},
Title = {Electrocardiographic J Wave and Cardiovascular Outcomes in
the General Population (from the Atherosclerosis Risk In
Communities Study).},
Journal = {The American journal of cardiology},
Volume = {118},
Number = {6},
Pages = {811-815},
Year = {2016},
Month = {September},
url = {http://dx.doi.org/10.1016/j.amjcard.2016.06.047},
Abstract = {The association between the J wave, a key component of the
early repolarization pattern, and adverse cardiovascular
outcomes remains unclear. Inconsistencies have stemmed from
the different methods used to measure the J wave. We
examined the association between the J wave, detected by an
automated method, and adverse cardiovascular outcomes in
14,592 (mean age = 54 ± 5.8 years; 56% women; 26% black)
participants from the Atherosclerosis Risk In Communities
(ARIC) study. The J wave was detected at baseline (1987 to
1989) and during follow-up study visits (1990 to 1992, 1993
to 1995, and 1996 to 1998) using a fully automated method.
Sudden cardiac death, coronary heart disease death, and
cardiovascular mortality were ascertained from hospital
discharge records, death certificates, and autopsy data
through December 31, 2010. A total of 278 participants
(1.9%) had evidence of a J wave. Over a median follow-up of
22 years, 4,376 of the participants (30%) died. In a
multivariable Cox regression analysis adjusted for
demographics, cardiovascular risk factors, and potential
confounders, the J wave was not associated with an increased
risk of sudden cardiac death (hazard ratio [HR] 0.74, 95% CI
0.36 to 1.50), coronary heart disease death (HR 0.72, 95% CI
0.40 to 1.32), or cardiovascular mortality (HR 1.16, 95% CI
0.87 to 1.56). An interaction was detected for
cardiovascular mortality by gender with men (HR 1.54, 95% CI
1.09 to 2.19) having a stronger association than women (HR
0.74, 95% CI 0.43 to 1.25; P-interaction = 0.030). In
conclusion, our findings suggest that the J wave is a benign
entity that is not associated with an increased risk for
sudden cardiac arrest in middle-aged adults in the United
States.},
Doi = {10.1016/j.amjcard.2016.06.047},
Key = {fds328302}
}
@article{fds329946,
Author = {Chui, CK and Lin, YT and Wu, HT},
Title = {Real-Time dynamics acquisition from irregular samples-With
application to anesthesia evaluation},
Journal = {Analysis and Applications},
Volume = {14},
Number = {4},
Pages = {537-590},
Publisher = {World Scientific Pub Co Pte Lt},
Year = {2016},
Month = {July},
url = {http://dx.doi.org/10.1142/S0219530515500165},
Abstract = {Although most digital representations of information sources
are obtained by uniform sampling of some continuous function
representations, there are many important events for which
only irregular data samples are available, including trading
data of the financial market and various clinical data, such
as the respiration signals hidden in ECG measurements. For
such digital information sources, the only available
effective smooth function interpolation scheme for
digital-To-Analog (D/A) conversion algorithms are mainly for
offline applications. Hence, in order to adapt the powerful
continuous-function mathematical approaches for real-Time
applications, it is necessary to introduce an effective D/A
conversion scheme as well as to modify the desired
continuous-function mathematical method for online
implementation. The powerful signal processing tool to be
discussed in this paper is the synchrosqueezed continuous
wavelet transform (SST), which requires computation of the
continuous wavelet transform (CWT), as well as its
derivative, of the analog signal of interest. An important
application of this transform is to extract information,
such as the underlying dynamics, hidden in the signal
representation. The first objective of this paper is to
introduce a unified approach to remove the two main
obstacles for adapting the SST approach to irregular data
samples in order to allow online computation. Firstly, for
D/A conversion, a real-Time algorithm, based on spline
functions of arbitrarily desired order, is proposed to
interpolate the irregular data samples, while preserving all
polynomials of the same spline order, with assured maximum
order of approximation. Secondly, for real-Time dynamic
information extraction from an oscillatory signal via SST, a
family of vanishing-moment and minimum-supported
spline-wavelets (to be called VM wavelets) are introduced
for online computation of the CWT and its derivative. The
second objective of this paper is to apply the proposed
real-Time algorithm and VM wavelets to clinical
applications, particularly to the study of the "anesthetic
depth" of a patient during surgery, with emphasis on
analyzing two dynamic quantities: The "instantaneous
frequencies" and the "non-rhythmic to rhythmic ratios" of
the patient's respiration, based on a one-lead
electrocardiogram (ECG) signal. Indeed, the "R-peaks" of the
ECG signal, which constitute a waveform landmark for
clinical evaluation, are non-uniform samples of the
respiratory signal. It is envisioned that the proposed
algorithm and VM wavelets should enable real-Time monitoring
of "anesthetic depth", during surgery, from the respiration
signal via ECG measurement.},
Doi = {10.1142/S0219530515500165},
Key = {fds329946}
}
@article{fds328303,
Author = {Daubechies, I and Wang, YG and Wu, H-T},
Title = {ConceFT: concentration of frequency and time via a
multitapered synchrosqueezed transform.},
Journal = {Philosophical transactions. Series A, Mathematical,
physical, and engineering sciences},
Volume = {374},
Number = {2065},
Pages = {20150193},
Year = {2016},
Month = {April},
url = {http://dx.doi.org/10.1098/rsta.2015.0193},
Abstract = {A new method is proposed to determine the time-frequency
content of time-dependent signals consisting of multiple
oscillatory components, with time-varying amplitudes and
instantaneous frequencies. Numerical experiments as well as
a theoretical analysis are presented to assess its
effectiveness.},
Doi = {10.1098/rsta.2015.0193},
Key = {fds328303}
}
@article{fds328823,
Author = {El Karoui and N and Wu, HT},
Title = {Graph connection Laplacian methods can be made robust to
noise},
Journal = {Annals of Statistics},
Volume = {44},
Number = {1},
Pages = {346-372},
Publisher = {Institute of Mathematical Statistics},
Year = {2016},
Month = {February},
url = {http://dx.doi.org/10.1214/14-AOS1275},
Abstract = {Recently, several data analytic techniques based on graph
connection Laplacian (GCL) ideas have appeared in the
literature. At this point, the properties of these methods
are starting to be understood in the setting where the data
is observed without noise. We study the impact of additive
noise on these methods and show that they are remarkably
robust. As a by-product of our analysis, we propose
modifications of the standard algorithms that increase their
robustness to noise. We illustrate our results in numerical
simulations.},
Doi = {10.1214/14-AOS1275},
Key = {fds328823}
}
@article{fds329947,
Author = {Herry, CL and Cortes, M and Wu, H-T and Durosier, LD and Cao, M and Burns,
P and Desrochers, A and Fecteau, G and Seely, AJE and Frasch,
MG},
Title = {Temporal Patterns in Sheep Fetal Heart Rate Variability
Correlate to Systemic Cytokine Inflammatory Response: A
Methodological Exploration of Monitoring Potential Using
Complex Signals Bioinformatics.},
Journal = {PloS one},
Volume = {11},
Number = {4},
Pages = {e0153515},
Year = {2016},
Month = {January},
url = {http://dx.doi.org/10.1371/journal.pone.0153515},
Abstract = {Fetal inflammation is associated with increased risk for
postnatal organ injuries. No means of early detection exist.
We hypothesized that systemic fetal inflammation leads to
distinct alterations of fetal heart rate variability (fHRV).
We tested this hypothesis deploying a novel series of
approaches from complex signals bioinformatics. In
chronically instrumented near-term fetal sheep, we induced
an inflammatory response with lipopolysaccharide (LPS)
injected intravenously (n = 10) observing it over 54 hours;
seven additional fetuses served as controls. Fifty-one fHRV
measures were determined continuously every 5 minutes using
Continuous Individualized Multi-organ Variability Analysis
(CIMVA). CIMVA creates an fHRV measures matrix across five
signal-analytical domains, thus describing complementary
properties of fHRV. We implemented, validated and tested
methodology to obtain a subset of CIMVA fHRV measures that
matched best the temporal profile of the inflammatory
cytokine IL-6. In the LPS group, IL-6 peaked at 3 hours. For
the LPS, but not control group, a sharp increase in
standardized difference in variability with respect to
baseline levels was observed between 3 h and 6 h abating to
baseline levels, thus tracking closely the IL-6 inflammatory
profile. We derived fHRV inflammatory index (FII) consisting
of 15 fHRV measures reflecting the fetal inflammatory
response with prediction accuracy of 90%. Hierarchical
clustering validated the selection of 14 out of 15 fHRV
measures comprising FII. We developed methodology to
identify a distinctive subset of fHRV measures that tracks
inflammation over time. The broader potential of this
bioinformatics approach is discussed to detect physiological
responses encoded in HRV measures.},
Doi = {10.1371/journal.pone.0153515},
Key = {fds329947}
}
@article{fds328824,
Author = {Wu, H-T and Wu, H-K and Wang, C-L and Yang, Y-L and Wu, W-H and Tsai, T-H and Chang, H-H},
Title = {Modeling the Pulse Signal by Wave-Shape Function and
Analyzing by Synchrosqueezing Transform.},
Journal = {PloS one},
Volume = {11},
Number = {6},
Pages = {e0157135},
Year = {2016},
Month = {January},
url = {http://dx.doi.org/10.1371/journal.pone.0157135},
Abstract = {We apply the recently developed adaptive non-harmonic model
based on the wave-shape function, as well as the
time-frequency analysis tool called synchrosqueezing
transform (SST) to model and analyze oscillatory
physiological signals. To demonstrate how the model and
algorithm work, we apply them to study the pulse wave
signal. By extracting features called the spectral pulse
signature, and based on functional regression, we
characterize the hemodynamics from the radial pulse wave
signals recorded by the sphygmomanometer. Analysis results
suggest the potential of the proposed signal processing
approach to extract health-related hemodynamics
features.},
Doi = {10.1371/journal.pone.0157135},
Key = {fds328824}
}
@article{fds342475,
Author = {Vatter, T and Wu, HT and Chavez-Demoulin, V and Yu,
B},
Title = {Non-parametric estimation of intraday spot volatility:
Disentangling instantaneous trend and seasonality},
Journal = {Econometrics},
Volume = {3},
Number = {4},
Pages = {864-887},
Year = {2015},
Month = {December},
url = {http://dx.doi.org/10.3390/econometrics3040864},
Abstract = {We provide a new framework for modeling trends and periodic
patterns in high-frequency financial data. Seeking
adaptivity to ever-changing market conditions, we enlarge
the Fourier flexible form into a richer functional class:
both our smooth trend and the seasonality are
non-parametrically time-varying and evolve in real time. We
provide the associated estimators and use simulations to
show that they behave adequately in the presence of jumps
and heteroskedastic and heavy-tailed noise. A study of
exchange rate returns sampled from 2010 to 2013 suggests
that failing to factor in the seasonality’s dynamic
properties may lead to misestimation of the intraday spot
volatility.},
Doi = {10.3390/econometrics3040864},
Key = {fds342475}
}
@article{fds328825,
Author = {Sheu, Y-L and Wu, H-T and Hsu, L-Y},
Title = {Exploring laser-driven quantum phenomena from a
time-frequency analysis perspective: a comprehensive
study.},
Journal = {Optics express},
Volume = {23},
Number = {23},
Pages = {30459-30482},
Year = {2015},
Month = {November},
url = {http://dx.doi.org/10.1364/oe.23.030459},
Abstract = {Time-frequency (TF) analysis is a powerful tool for
exploring ultrafast dynamics in atoms and molecules. While
some TF methods have demonstrated their usefulness and
potential in several quantum systems, a systematic
comparison among them is still lacking. To this end, we
compare a series of classical and contemporary TF methods by
taking hydrogen atom in a strong laser field as a benchmark.
In addition, several TF methods such as Cohen class
distribution other than the Wigner-Ville distribution,
reassignment methods, and the empirical mode decomposition
method are first introduced to exploration of ultrafast
dynamics. Among these TF methods, the synchrosqueezing
transform successfully illustrates the physical mechanisms
in the multiphoton ionization regime and in the tunneling
ionization regime. Furthermore, an empirical procedure to
analyze an unknown complicated quantum system is provided,
suggesting the versatility of TF analysis as a new viable
venue for exploring quantum dynamics.},
Doi = {10.1364/oe.23.030459},
Key = {fds328825}
}
@article{fds328827,
Author = {Lederman, RR and Talmon, R and Wu, HT and Lo, YL and Coifman,
RR},
Title = {Alternating diffusion for common manifold learning with
application to sleep stage assessment},
Journal = {ICASSP, IEEE International Conference on Acoustics, Speech
and Signal Processing - Proceedings},
Volume = {2015-August},
Pages = {5758-5762},
Publisher = {IEEE},
Year = {2015},
Month = {August},
ISBN = {9781467369978},
url = {http://dx.doi.org/10.1109/ICASSP.2015.7179075},
Abstract = {In this paper, we address the problem of multimodal signal
processing and present a manifold learning method to extract
the common source of variability from multiple measurements.
This method is based on alternating-diffusion and is
particularly adapted to time series. We show that the common
source of variability is extracted from multiple sensors as
if it were the only source of variability, extracted by a
standard manifold learning method from a single sensor,
without the influence of the sensor-specific variables. In
addition, we present application to sleep stage assessment.
We demonstrate that, indeed, through alternating-diffusion,
the sleep information hidden inside multimodal respiratory
signals can be better captured compared to single-modal
methods.},
Doi = {10.1109/ICASSP.2015.7179075},
Key = {fds328827}
}
@article{fds328826,
Author = {Wu, H-T and Talmon, R and Lo, Y-L},
Title = {Assess sleep stage by modern signal processing
techniques.},
Journal = {IEEE transactions on bio-medical engineering},
Volume = {62},
Number = {4},
Pages = {1159-1168},
Year = {2015},
Month = {April},
url = {http://dx.doi.org/10.1109/tbme.2014.2375292},
Abstract = {In this paper, two modern adaptive signal processing
techniques, empirical intrinsic geometry and
synchrosqueezing transform, are applied to quantify
different dynamical features of the respiratory and
electroencephalographic signals. We show that the proposed
features are theoretically rigorously supported, as well as
capture the sleep information hidden inside the signals. The
features are used as input to multiclass support vector
machines with the radial basis function to automatically
classify sleep stages. The effectiveness of the
classification based on the proposed features is shown to be
comparable to human expert classification-the proposed
classification of awake, REM, N1, N2, and N3 sleeping stages
based on the respiratory signal (resp. respiratory and EEG
signals) has the overall accuracy 81.7% (resp. 89.3%) in the
relatively normal subject group. In addition, by examining
the combination of the respiratory signal with the
electroencephalographic signal, we conclude that the
respiratory signal consists of ample sleep information,
which supplements to the information stored in the
electroencephalographic signal.},
Doi = {10.1109/tbme.2014.2375292},
Key = {fds328826}
}
@article{fds346285,
Author = {Karoui, NE and Wu, HT},
Title = {Graph connection Laplacian and random matrices with random
blocks},
Journal = {Information and Inference},
Volume = {4},
Number = {1},
Pages = {1-42},
Year = {2015},
Month = {March},
url = {http://dx.doi.org/10.1093/imaiai/iav001},
Abstract = {Graph connection Laplacian (GCL) is a modern data analysis
technique that is starting to be applied for the analysis of
high-dimensional and massive datasets. Motivated by this
technique, we study matrices that are akin to the ones
appearing in the null case of GCL, i.e. the case where there
is no structure in the dataset under investigation.
Developing this understanding is important in making sense
of the output of the algorithms based on GCL. We hence
develop a theory explaining the behavior of the spectral
distribution of a large class of random matrices, in
particular random matrices with random block entries of
fixed size. Part of the theory covers the case where there
is significant dependence between the blocks. Numerical work
shows that the agreement between our theoretical predictions
and numerical simulations is generally very
good.},
Doi = {10.1093/imaiai/iav001},
Key = {fds346285}
}
@article{fds341878,
Author = {Wu, HT and Baudin, F and Frasch, MG and Emeriaud,
G},
Title = {Respiratory variability during NAVA ventilation in children:
Authors' reply},
Journal = {Frontiers in Pediatrics},
Volume = {3},
Number = {FEB},
Year = {2015},
Month = {February},
url = {http://dx.doi.org/10.3389/fped.2015.00013},
Doi = {10.3389/fped.2015.00013},
Key = {fds341878}
}
@article{fds328304,
Author = {Wang, YG and Wu, H-T and Daubechies, I and Li, Y and Estes, EH and Soliman,
EZ},
Title = {Automated J wave detection from digital 12-lead
electrocardiogram.},
Journal = {Journal of electrocardiology},
Volume = {48},
Number = {1},
Pages = {21-28},
Year = {2015},
Month = {January},
url = {http://dx.doi.org/10.1016/j.jelectrocard.2014.10.006},
Abstract = {In this report we provide a method for automated detection
of J wave, defined as a notch or slur in the descending
slope of the terminal positive wave of the QRS complex,
using signal processing and functional data analysis
techniques. Two different sets of ECG tracings were selected
from the EPICARE ECG core laboratory, Wake Forest School of
Medicine, Winston Salem, NC. The first set was a training
set comprised of 100 ECGs of which 50 ECGs had J-wave and
the other 50 did not. The second set was a test set (n=116
ECGs) in which the J-wave status (present/absent) was only
known by the ECG Center staff. All ECGs were recorded using
GE MAC 1200 (GE Marquette, Milwaukee, Wisconsin) at 10mm/mV
calibration, speed of 25mm/s and 500HZ sampling rate. All
ECGs were initially inspected visually for technical errors
and inadequate quality, and then automatically processed
with the GE Marquette 12-SL program 2001 version (GE
Marquette, Milwaukee, WI). We excluded ECG tracings with
major abnormalities or rhythm disorder. Confirmation of the
presence or absence of a J wave was done visually by the ECG
Center staff and verified once again by three of the
coauthors. There was no disagreement in the identification
of the J wave state. The signal processing and functional
data analysis techniques applied to the ECGs were conducted
at Duke University and the University of Toronto. In the
training set, the automated detection had sensitivity of
100% and specificity of 94%. For the test set, sensitivity
was 89% and specificity was 86%. In conclusion, test results
of the automated method we developed show a good J wave
detection accuracy, suggesting possible utility of this
approach for defining and detection of other complex ECG
waveforms.},
Doi = {10.1016/j.jelectrocard.2014.10.006},
Key = {fds328304}
}
@article{fds333769,
Author = {Lederman, RR and Talmon, R and Wu, H-T and Lo, Y-L and Coifman,
RR},
Title = {ALTERNATING DIFFUSION FOR COMMON MANIFOLD LEARNING WITH
APPLICATION TO SLEEP STAGE ASSESSMENT},
Journal = {2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND
SIGNAL PROCESSING (ICASSP)},
Pages = {5758-5762},
Year = {2015},
Key = {fds333769}
}
@article{fds341879,
Author = {Baudin, F and Wu, HT and Bordessoule, A and Beck, J and Jouvet, P and Frasch, MG and Emeriaud, G},
Title = {Impact of ventilatory modes on the breathing variability in
mechanically ventilated infants},
Journal = {Frontiers in Pediatrics},
Volume = {2},
Number = {NOV},
Year = {2014},
Month = {November},
url = {http://dx.doi.org/10.3389/fped.2014.00132},
Abstract = {Objectives: Reduction of breathing variability is associated
with adverse outcome. During mechanical ventilation, the
variability of ventilatory pressure is dependent on the
ventilatory mode. During neurally adjusted ventilatory
assist (NAVA), the support is proportional to electrical
activity of the diaphragm (EAdi), which reflects the
respiratory center output. The variability of EAdi is,
therefore, translated into a similar variability in
pressures. Contrastingly, conventional ventilatory modes
deliver less variable pressures. The impact of the mode on
the patient's own respiratory drive is less clear. This
study aims to compare the impact of NAVA,
pressure-controlled ventilation (PCV), and pressure support
ventilation (PSV) on the respiratory drive patterns in
infants. We hypothesized that on NAVA, EAdi variability
resembles most of the endogenous respiratory drive pattern
seen in a control group. Methods: Electrical activity of the
diaphragm was continuously recorded in 10 infants ventilated
successively on NAVA (5 h), PCV (30 min), and PSV (30 min).
During the last 10 min of each period, the EAdi variability
pattern was assessed using non-rhythmic to rhythmic (NRR)
index. These variability profiles were compared to the
pattern of a control group of 11 spontaneously breathing and
non-intubated infants. Results: In control infants, NRR was
higher as compared to mechanically ventilated infants (p <
0.001), and NRR pattern was relatively stable over time.
While the temporal stability of NRR was similar in NAVA and
controls, the NRR profile was less stable during PCV. PSV
exhibited an intermediary pattern. Perspectives: Mechanical
ventilation impacts the breathing variability in infants.
NAVA produces EAdi pattern resembling most that of control
infants. NRR can be used to characterize respiratory
variability in infants. Larger prospective studies are
necessary to understand the differential impact of the
ventilatory modes on the cardio-respiratory variability and
to study their impact on clinical outcomes.},
Doi = {10.3389/fped.2014.00132},
Key = {fds341879}
}
@article{fds328828,
Author = {Sheu, YL and Hsu, LY and Wu, HT and Li, PC and Chu, SI},
Title = {A new time-frequency method to reveal quantum dynamics of
atomic hydrogen in intense laser pulses: Synchrosqueezing
transform},
Journal = {AIP Advances},
Volume = {4},
Number = {11},
Pages = {117138-117138},
Publisher = {AIP Publishing},
Year = {2014},
Month = {November},
url = {http://dx.doi.org/10.1063/1.4903164},
Abstract = {This study introduces a new adaptive time-frequency (TF)
analysis technique, the synchrosqueezing transform (SST), to
explore the dynamics of a laser-driven hydrogen atom at an
ab initio level, upon which we have demonstrated its
versatility as a new viable venue for further exploring
quantum dynamics. For a signal composed of oscillatory
components which can be characterized by instantaneous
frequency, the SST enables rendering the decomposed signal
based on the phase information inherited in the linear TF
representation with mathematical support. Compared with the
classical type of TF methods, the SST clearly depicts
several intrinsic quantum dynamical processes such as
selection rules, AC Stark effects, and high harmonic
generation.},
Doi = {10.1063/1.4903164},
Key = {fds328828}
}
@article{fds328305,
Author = {Wu, H-T and Hseu, S-S and Bien, M-Y and Kou, YR and Daubechies,
I},
Title = {Evaluating physiological dynamics via synchrosqueezing:
prediction of ventilator weaning.},
Journal = {IEEE transactions on bio-medical engineering},
Volume = {61},
Number = {3},
Pages = {736-744},
Year = {2014},
Month = {March},
url = {http://dx.doi.org/10.1109/tbme.2013.2288497},
Abstract = {Oscillatory phenomena abound in many types of signals.
Identifying the individual oscillatory components that
constitute an observed biological signal leads to profound
understanding about the biological system. The instantaneous
frequency (IF), the amplitude modulation (AM), and their
temporal variability are widely used to describe these
oscillatory phenomena. In addition, the shape of the
oscillatory pattern, repeated in time for an oscillatory
component, is also an important characteristic that can be
parametrized appropriately. These parameters can be viewed
as phenomenological surrogates for the hidden dynamics of
the biological system. To estimate jointly the IF, AM, and
shape, this paper applies a novel and robust time-frequency
analysis tool, referred to as the synchrosqueezing transform
(SST). The usefulness of the model and SST are shown
directly in predicting the clinical outcome of ventilator
weaning. Compared with traditional respiration parameters,
the breath-to-breath variability has been reported to be a
better predictor of the outcome of the weaning procedure. So
far, however, all these indices normally require at least 20
min of data acquisition to ensure predictive power.
Moreover, the robustness of these indices to the inevitable
noise is rarely discussed. We find that based on the
proposed model, SST and only 3 min of respiration data, the
ROC area under curve of the prediction accuracy is 0.76. The
high predictive power that is achieved in the weaning
problem, despite a shorter evaluation period, and the
stability to noise suggest that other similar kinds of
signal may likewise benefit from the proposed model and
SST.},
Doi = {10.1109/tbme.2013.2288497},
Key = {fds328305}
}
@article{fds328830,
Author = {Wu, HT and Chan, YH and Lin, YT and Yeh, YH},
Title = {Using synchrosqueezing transform to discover breathing
dynamics from ECG signals},
Journal = {Applied and Computational Harmonic Analysis},
Volume = {36},
Number = {2},
Pages = {354-359},
Publisher = {Elsevier BV},
Year = {2014},
Month = {March},
url = {http://dx.doi.org/10.1016/j.acha.2013.07.003},
Abstract = {The acquisition of breathing dynamics without directly
recording the respiratory signals is beneficial in many
clinical settings. The electrocardiography (ECG)-derived
respiration (EDR) algorithm enables data acquisition in this
manner. However, the EDR algorithm fails in analyzing such
data for patients with atrial fibrillation (AF) because of
their highly irregular heart rates. To resolve these
problems, we introduce a new algorithm, referred to as
SSTEDR, to extract the breathing dynamics directly from the
single lead ECG signal; it is based on the EDR algorithm and
the time-frequency representation technique referred to as
the synchrosqueezing transform. We report a preliminary
result about the relationship between the anesthetic depth
and breathing dynamics. To the best of our knowledge, this
is the first algorithm allowing us to extract the breathing
dynamics of patients with obvious AF from the single lead
ECG signal. © 2013 Elsevier Inc.},
Doi = {10.1016/j.acha.2013.07.003},
Key = {fds328830}
}
@article{fds328831,
Author = {Lin, Y-T and Wu, H-T and Tsao, J and Yien, H-W and Hseu,
S-S},
Title = {Time-varying spectral analysis revealing differential
effects of sevoflurane anaesthesia: non-rhythmic-to-rhythmic
ratio.},
Journal = {Acta anaesthesiologica Scandinavica},
Volume = {58},
Number = {2},
Pages = {157-167},
Year = {2014},
Month = {February},
url = {http://dx.doi.org/10.1111/aas.12251},
Abstract = {<h4>Background</h4>Heart rate variability (HRV) may reflect
various physiological dynamics. In particular, variation of
R-R peak interval (RRI) of electrocardiography appears
regularly oscillatory in deeper levels of anaesthesia and
less regular in lighter levels of anaesthesia. We proposed a
new index, non-rhythmic-to-rhythmic ratio (NRR), to quantify
this feature and investigated its potential to estimate
depth of anaesthesia.<h4>Methods</h4>Thirty-one female
patients were enrolled in this prospective study. The
oscillatory pattern transition of RRI was visualised by the
time-varying power spectrum and quantified by NRR. The
prediction of anaesthetic events, including skin incision,
first reaction of motor movement during emergence period,
loss of consciousness (LOC) and return of consciousness
(ROC) by NRR were evaluated by serial prediction probability
(PK ) analysis; the ability to predict the decrease of
effect-site sevoflurane concentration was also evaluated.
The results were compared with Bispectral Index
(BIS).<h4>Results</h4>NRR well-predicted first reaction (PK
> 0.90) 30 s ahead, earlier than BIS and
significantly better than HRV indices. NRR well-correlated
with sevoflurane concentration, although its correlation was
inferior to BIS, while HRV indices had no such correlation.
BIS indicated LOC and ROC best.<h4>Conclusions</h4>Our
findings suggest that NRR provides complementary information
to BIS regarding the differential effects of anaesthetics on
the brain, especially the subcortical motor
activity.},
Doi = {10.1111/aas.12251},
Key = {fds328831}
}
@article{fds328829,
Author = {Chen, YC and Cheng, MY and Wu, HT},
Title = {Non-parametric and adaptive modelling of dynamic periodicity
and trend with heteroscedastic and dependent
errors},
Journal = {Journal of the Royal Statistical Society. Series B:
Statistical Methodology},
Volume = {76},
Number = {3},
Pages = {651-682},
Publisher = {WILEY},
Year = {2014},
Month = {January},
url = {http://dx.doi.org/10.1111/rssb.12039},
Abstract = {Periodicity and trend are features describing an observed
sequence, and extracting these features is an important
issue in many scientific fields. However, it is not an easy
task for existing methods to analyse simultaneously the
trend and dynamics of the periodicity such as time varying
frequency and amplitude, and the adaptivity of the analysis
to such dynamics and robustness to heteroscedastic dependent
errors are not guaranteed. These tasks become even more
challenging when there are multiple periodic components. We
propose a non-parametric model to describe the dynamics of
multicomponent periodicity and investigate the recently
developed synchro-squeezing transform in extracting these
features in the presence of a trend and heteroscedastic
dependent errors. The identifiability problem of the
non-parametric periodicity model is studied, and the
adaptivity and robustness properties of the
synchro-squeezing transform are theoretically justified in
both discrete and continuous time settings. Consequently we
have a new technique for decoupling the trend, periodicity
and heteroscedastic, dependent error process in a general
non-parametric set-up. Results of a series of simulations
are provided, and the incidence time series of varicella and
herpes zoster in Taiwan and respiratory signals observed
from a sleep study are analysed. © 2013 Royal Statistical
Society.},
Doi = {10.1111/rssb.12039},
Key = {fds328829}
}
@article{fds328833,
Author = {Marchesini, S and Schirotzek, A and Yang, C and Wu, HT and Maia,
F},
Title = {Augmented projections for ptychographic imaging},
Journal = {Inverse Problems},
Volume = {29},
Number = {11},
Pages = {115009-115009},
Publisher = {IOP Publishing},
Year = {2013},
Month = {November},
url = {http://dx.doi.org/10.1088/0266-5611/29/11/115009},
Abstract = {Ptychography is a popular technique to achieve diffraction
limited resolution images of a two- or three-dimensional
sample using high frame rate detectors. We introduce a
relaxation of common projection algorithms to account for
instabilities given by intensity and background
fluctuations, position errors, or poor calibration using
multiplexing illumination. This relaxation introduces an
additional phasing optimization at every step that enhances
the convergence rate of common projection algorithms.
Numerical tests exhibit the exact recovery of the object and
the perturbations when there is high redundancy in the data.
© 2013 IOP Publishing Ltd.},
Doi = {10.1088/0266-5611/29/11/115009},
Key = {fds328833}
}
@article{fds361351,
Author = {Zhang, J-T and Cheng, M-Y and Tseng, C-J and Wu, H-T},
Title = {A New Test for One-Way ANOVA with Functional Data and
Application to Ischemic Heart Screening},
Year = {2013},
Month = {September},
Abstract = {We propose and study a new global test, namely the
$F_{\max}$-test, for the one-way ANOVA problem in functional
data analysis. The test statistic is taken as the maximum
value of the usual pointwise $F$-test statistics over the
interval the functional responses are observed. A
nonparametric bootstrap method is employed to approximate
the null distribution of the test statistic and to obtain an
estimated critical value for the test. The asymptotic random
expression of the test statistic is derived and the
asymptotic power is studied. In particular, under mild
conditions, the $F_{\max}$-test asymptotically has the
correct level and is root-$n$ consistent in detecting local
alternatives. Via some simulation studies, it is found that
in terms of both level accuracy and power, the
$F_{\max}$-test outperforms the Globalized Pointwise F (GPF)
test of \cite{Zhang_Liang:2013} when the functional data are
highly or moderately correlated, and its performance is
comparable with the latter otherwise. An application to an
ischemic heart real dataset suggests that, after proper
manipulation, resting electrocardiogram (ECG) signals can be
used as an effective tool in clinical ischemic heart
screening, without the need of further stress tests as in
the current standard procedure.},
Key = {fds361351}
}
@article{fds328835,
Author = {Wu, HT},
Title = {Instantaneous frequency and wave shape functions
(I)},
Journal = {Applied and Computational Harmonic Analysis},
Volume = {35},
Number = {2},
Pages = {181-199},
Publisher = {Elsevier BV},
Year = {2013},
Month = {September},
url = {http://dx.doi.org/10.1016/j.acha.2012.08.008},
Abstract = {Although one can formulate an intuitive notion of
instantaneous frequency, generalizing "frequency" as we
understand it in e.g. the Fourier transform, a rigorous
mathematical definition is lacking. In this paper, we
consider a class of functions composed of waveforms that
repeat nearly periodically, and for which the instantaneous
frequency can be given a rigorous meaning. We show that
Synchrosqueezing can be used to determine the instantaneous
frequency of functions in this class, even if the waveform
is not harmonic, thus generalizing earlier results for
cosine wave functions. We also provide real-life examples
and discuss the advantages, for these examples, of
considering such non-harmonic waveforms. © 2012 Elsevier
Inc.},
Doi = {10.1016/j.acha.2012.08.008},
Key = {fds328835}
}
@article{fds328836,
Author = {Thakur, G and Brevdo, E and Fučkar, NS and Wu, HT},
Title = {The Synchrosqueezing algorithm for time-varying spectral
analysis: Robustness properties and new paleoclimate
applications},
Journal = {Signal Processing},
Volume = {93},
Number = {5},
Pages = {1079-1094},
Publisher = {Elsevier BV},
Year = {2013},
Month = {May},
url = {http://dx.doi.org/10.1016/j.sigpro.2012.11.029},
Abstract = {We analyze the stability properties of the Synchrosqueezing
transform, a time-frequency signal analysis method that can
identify and extract oscillatory components with
time-varying frequency and amplitude. We show that
Synchrosqueezing is robust to bounded perturbations of the
signal and to Gaussian white noise. These results justify
its applicability to noisy or nonuniformly sampled data that
is ubiquitous in engineering and the natural sciences. We
also describe a practical implementation of Synchrosqueezing
and provide guidance on tuning its main parameters. As a
case study in the geosciences, we examine characteristics of
a key paleoclimate change in the last 2.5 million years,
where Synchrosqueezing provides significantly improved
insights. © 2012 Elsevier B.V. All rights
reserved.},
Doi = {10.1016/j.sigpro.2012.11.029},
Key = {fds328836}
}
@article{fds328832,
Author = {Cheng, MY and Wu, HT},
Title = {Local linear regression on manifolds and its geometric
interpretation},
Journal = {Journal of the American Statistical Association},
Volume = {108},
Number = {504},
Pages = {1421-1434},
Publisher = {Informa UK Limited},
Year = {2013},
Month = {January},
url = {http://dx.doi.org/10.1080/01621459.2013.827984},
Abstract = {High-dimensional data analysis has been an active area, and
the main focus areas have been variable selection and
dimension reduction. In practice, it occurs often that the
variables are located on an unknown, lower-dimensional
nonlinear manifold. Under this manifold assumption, one
purpose of this article is regression and gradient
estimation on the manifold, and another is developing a new
tool for manifold learning. As regards the first aim, we
suggest directly reducing the dimensionality to the
intrinsic dimension d of the manifold, and performing the
popular local linear regression (LLR) on a tangent plane
estimate. An immediate consequence is a dramatic reduction
in the computational time when the ambient space dimension p
≫ d. We provide rigorous theoretical justification of the
convergence of the proposed regression and gradient
estimators by carefully analyzing the curvature, boundary,
and nonuniform sampling effects. We propose a bandwidth
selector that can handle heteroscedastic errors.With
reference to the second aim, we analyze carefully the
asymptotic behavior of our regression estimator both in the
interior and near the boundary of the manifold, and make
explicit its relationship with manifold learning, in
particular estimating the Laplace-Beltrami operator of the
manifold. In this context, we also make clear that it is
important to use a smaller bandwidth in the tangent plane
estimation than in the LLR. A simulation study and
applications to the Isomap face data and a clinically
computed tomography scan dataset are used to illustrate the
computational speed and estimation accuracy of our methods.
Supplementary materials for this article are available
online. © 2013 American Statistical Association.},
Doi = {10.1080/01621459.2013.827984},
Key = {fds328832}
}
@article{fds328834,
Author = {Auger, F and Flandrin, P and Lin, YT and McLaughlin, S and Meignen, S and Oberlin, T and Wu, HT},
Title = {Time-frequency reassignment and synchrosqueezing: An
overview},
Journal = {IEEE Signal Processing Magazine},
Volume = {30},
Number = {6},
Pages = {32-41},
Publisher = {Institute of Electrical and Electronics Engineers
(IEEE)},
Year = {2013},
Month = {January},
url = {http://dx.doi.org/10.1109/MSP.2013.2265316},
Abstract = {This article provides a general overview of time-frequency
(T-F) reassignment and synchrosqueezing techniques applied
to multicomponent signals, covering the theoretical
background and applications. We explain how synchrosqueezing
can be viewed as a special case of reassignment enabling
mode reconstruction and place emphasis on the interest of
using such T-F distributions throughout with illustrative
examples. © 1991-2012 IEEE.},
Doi = {10.1109/MSP.2013.2265316},
Key = {fds328834}
}
@article{fds328837,
Author = {Wang, Y-M and Wu, H-T and Huang, E-Y and Kou, YR and Hseu,
S-S},
Title = {Heart rate variability is associated with survival in
patients with brain metastasis: a preliminary
report.},
Journal = {BioMed research international},
Volume = {2013},
Pages = {503421},
Year = {2013},
Month = {January},
url = {http://dx.doi.org/10.1155/2013/503421},
Abstract = {Impaired heart rate variability (HRV) has been demonstrated
as a negative survival prognosticator in various diseases.
We conducted this prospective study to evaluate how HRV
affects brain metastasis (BM) patients. Fifty-one BM
patients who had not undergone previous brain operation or
radiotherapy (RT) were recruited from January 2010 to July
2012, and 40 patients were included in the final analysis. A
5-minute electrocardiogram was obtained before whole brain
radiotherapy. Time domain indices of HRV were compared with
other clinical factors on overall survival (OS). In the
univariate analysis, Karnofsky performance status (KPS) <70
(P = 0.002) and standard deviation of the normal-to-normal
interval (SDNN) <10 ms (P = 0.004) significantly predict
poor survival. The multivariate analysis revealed that KPS
<70 and SDNN <10 ms were independent negative
prognosticators for survival in BM patients with hazard
ratios of 2.657 and 2.204, respectively. In conclusion, HRV
is associated with survival and may be a novel prognostic
factor for BM patients.},
Doi = {10.1155/2013/503421},
Key = {fds328837}
}
@article{fds328838,
Author = {Singer, A and Wu, H-T},
Title = {Two-Dimensional Tomography from Noisy Projections Taken at
Unknown Random Directions.},
Journal = {SIAM journal on imaging sciences},
Volume = {6},
Number = {1},
Pages = {136-175},
Year = {2013},
Month = {January},
url = {http://dx.doi.org/10.1137/090764657},
Abstract = {Computerized tomography is a standard method for obtaining
internal structure of objects from their projection images.
While CT reconstruction requires the knowledge of the
imaging directions, there are some situations in which the
imaging directions are unknown, for example, when imaging a
moving object. It is therefore desirable to design a
reconstruction method from projection images taken at
unknown directions. Another difficulty arises from the fact
that the projections are often contaminated by noise,
practically limiting all current methods, including the
recently proposed diffusion map approach. In this paper, we
introduce two denoising steps that allow reconstructions at
much lower signal-to-noise ratios (SNRs) when combined with
the diffusion map framework. In the first denoising step we
use principal component analysis (PCA) together with
classical Wiener filtering to derive an asymptotically
optimal linear filter. In the second step, we denoise the
graph of similarities between the filtered projections using
a network analysis measure such as the Jaccard index. Using
this combination of PCA, Wiener filtering, graph denoising,
and diffusion maps, we are able to reconstruct the
two-dimensional (2-D) Shepp-Logan phantom from simulative
noisy projections at SNRs well below their currently
reported threshold values. We also report the results of a
numerical experiment corresponding to an abdominal CT.
Although the focus of this paper is the 2-D CT
reconstruction problem, we believe that the combination of
PCA, Wiener filtering, graph denoising, and diffusion maps
is potentially useful in other signal processing and image
analysis applications.},
Doi = {10.1137/090764657},
Key = {fds328838}
}
@article{fds328839,
Author = {Singer, A and Wu, H-T},
Title = {Vector Diffusion Maps and the Connection
Laplacian.},
Journal = {Communications on pure and applied mathematics},
Volume = {65},
Number = {8},
Year = {2012},
Month = {August},
url = {http://dx.doi.org/10.1002/cpa.21395},
Abstract = {We introduce <i>vector diffusion maps</i> (VDM), a new
mathematical framework for organizing and analyzing massive
high-dimensional data sets, images, and shapes. VDM is a
mathematical and algorithmic generalization of diffusion
maps and other nonlinear dimensionality reduction methods,
such as LLE, ISOMAP, and Laplacian eigenmaps. While existing
methods are either directly or indirectly related to the
heat kernel for functions over the data, VDM is based on the
heat kernel for vector fields. VDM provides tools for
organizing complex data sets, embedding them in a
low-dimensional space, and interpolating and regressing
vector fields over the data. In particular, it equips the
data with a metric, which we refer to as the <i>vector
diffusion distance</i>. In the manifold learning setup,
where the data set is distributed on a low-dimensional
manifold ℳ <i><sup>d</sup></i> embedded in ℝ
<sup><i>p</i></sup> , we prove the relation between VDM and
the connection Laplacian operator for vector fields over the
manifold.},
Doi = {10.1002/cpa.21395},
Key = {fds328839}
}
@article{fds328841,
Author = {Thakur, G and Wu, HT},
Title = {Synchrosqueezing-based recovery of instantaneous frequency
from nonuniform samples},
Journal = {SIAM Journal on Mathematical Analysis},
Volume = {43},
Number = {5},
Pages = {2078-2095},
Publisher = {Society for Industrial & Applied Mathematics
(SIAM)},
Year = {2011},
Month = {November},
url = {http://dx.doi.org/10.1137/100798818},
Abstract = {We propose a new approach for studying the notion of the
instantaneous frequency of a signal. We build on ideas from
the Synchrosqueezing theory of Daubechies, Lu, andWu [Appl.
Comput. Harmonic Anal., 30 (2010), pp. 243-261] and consider
a variant of Synchrosqueezing, based on the short-time
Fourier transform, to precisely define the instantaneous
frequencies of a multicomponent AM-FM signal. We describe an
algorithm to recover these instantaneous frequencies from
the uniform or nonuniform samples of the signal and show
that our method is robust to noise. We also consider an
alternative approach based on the conventional, Hilbert
transform-based notion of instantaneous frequency to compare
to our new method. We use these methods on several test
cases and apply our results to a signal analysis problem in
electrocardiography. © 2011 Society for Industrial and
Applied Mathematics.},
Doi = {10.1137/100798818},
Key = {fds328841}
}
@article{fds328840,
Author = {Singer, A and Wu, H-T},
Title = {Orientability and Diffusion Maps.},
Journal = {Applied and computational harmonic analysis},
Volume = {31},
Number = {1},
Pages = {44-58},
Year = {2011},
Month = {July},
url = {http://dx.doi.org/10.1016/j.acha.2010.10.001},
Abstract = {One of the main objectives in the analysis of a high
dimensional large data set is to learn its geometric and
topological structure. Even though the data itself is
parameterized as a point cloud in a high dimensional ambient
space ℝ(p), the correlation between parameters often
suggests the "manifold assumption" that the data points are
distributed on (or near) a low dimensional Riemannian
manifold ℳ(d) embedded in ℝ(p), with d ≪ p. We
introduce an algorithm that determines the orientability of
the intrinsic manifold given a sufficiently large number of
sampled data points. If the manifold is orientable, then our
algorithm also provides an alternative procedure for
computing the eigenfunctions of the Laplacian that are
important in the diffusion map framework for reducing the
dimensionality of the data. If the manifold is
non-orientable, then we provide a modified diffusion mapping
of its orientable double covering.},
Doi = {10.1016/j.acha.2010.10.001},
Key = {fds328840}
}
@article{fds328307,
Author = {Wu, HT and Flandrin, P and Daubechies, I},
Title = {One or two frequencies? the synchrosqueezing
answers},
Journal = {Advances in Adaptive Data Analysis},
Volume = {3},
Number = {1-2},
Pages = {29-39},
Publisher = {World Scientific Pub Co Pte Lt},
Year = {2011},
Month = {April},
url = {http://dx.doi.org/10.1142/S179353691100074X},
Abstract = {The synchrosqueezed transform was proposed recently in
[Daubechies et al. (2009)] as an alternative to the
empirical mode decomposition (EMD) [Huang et al. (1998)], to
decompose composite signals into a sum of "modes" that each
have well-defined instantaneous frequencies. This paper
presents, for synchrosqueezing, a study similar to that in
[Rilling and Flandrin (2008)] for EMD, of how two signals
with close frequencies are recognized and represented as
such. © 2011 World Scientific Publishing
Company.},
Doi = {10.1142/S179353691100074X},
Key = {fds328307}
}
@article{fds328306,
Author = {Daubechies, I and Lu, J and Wu, H-T},
Title = {Synchrosqueezed wavelet transforms: An empirical mode
decomposition-like tool},
Journal = {Applied and Computational Harmonic Analysis},
Volume = {30},
Number = {2},
Pages = {243-261},
Publisher = {Elsevier BV},
Year = {2011},
Month = {March},
url = {http://dx.doi.org/10.1016/j.acha.2010.08.002},
Abstract = {The EMD algorithm is a technique that aims to decompose into
their building blocks functions that are the superposition
of a (reasonably) small number of components, well separated
in the time-frequency plane, each of which can be viewed as
approximately harmonic locally, with slowly varying
amplitudes and frequencies. The EMD has already shown its
usefulness in a wide range of applications including
meteorology, structural stability analysis, medical studies.
On the other hand, the EMD algorithm contains heuristic and
ad hoc elements that make it hard to analyze mathematically.
In this paper we describe a method that captures the flavor
and philosophy of the EMD approach, albeit using a different
approach in constructing the components. The proposed method
is a combination of wavelet analysis and reallocation
method. We introduce a precise mathematical definition for a
class of functions that can be viewed as a superposition of
a reasonably small number of approximately harmonic
components, and we prove that our method does indeed succeed
in decomposing arbitrary functions in this class. We provide
several examples, for simulated as well as real data. ©
2010 Elsevier Inc. All rights reserved.},
Doi = {10.1016/j.acha.2010.08.002},
Key = {fds328306}
}
|
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Mathematics Department