Meniere's disease prognosis by learning from transient-evoked otoacoustic emission signals

Sheng Lun Kao, Han Wen Lien, Tzu Chi Liu, Hau Tieng Wu, Te Yung Fang, Pa Chun Wang, Yi Wen Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationProceedings of the 23rd International Congress on Acoustics
Subtitle of host publicationIntegrating 4th EAA Euroregio 2019
EditorsMartin Ochmann, Vorlander Michael, Janina Fels
PublisherInternational Commission for Acoustics (ICA)
Number of pages8
ISBN (Electronic)9783939296157
StatePublished - 2019
Event23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019 - Aachen, Germany
Duration: Sep 9 2019Sep 23 2019

Publication series

NameProceedings of the International Congress on Acoustics
ISSN (Print)2226-7808
ISSN (Electronic)2415-1599


Conference23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019


  • Machine Learning
  • Meniere's Disease
  • Otoacoustic Emission
  • Signal Processing

ASJC Scopus subject areas

  • Mechanical Engineering
  • Acoustics and Ultrasonics


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