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.
- acute sensorineural hearing loss
- machine learning
- Ménière’s disease
- Transient-evoked otoacoustic emission
ASJC Scopus subject areas