Publications [#348869] of Hau-Tieng Wu

Papers Published

1. Liu, Y-W; Kao, S-L; Wu, H-T; Liu, T-C; Fang, T-Y; Wang, P-C, Transient-evoked otoacoustic emission signals predicting outcomes of acute sensorineural hearing loss in patients with Ménière's disease., Acta oto-laryngologica, vol. 140 no. 3 (March, 2020), pp. 230-235 [doi]
   (last updated on 2024/04/24)

   Abstract:
   Background: 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.Aims/objectives: To find parameters for predicting MD hearing outcomes.Material and methods: 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.Results: Signal energy did not differ (p = .64) but a significant difference in 1-kHz (p = .045) group delay was recorded between improved and nonimproved groups. The SVM achieved a cross-validated accuracy of >80% in predicting hearing outcomes.Conclusions and significance: 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.