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Background: Fluctuating hearing loss is characteristic of Meniere's Disease(MD) during acute episodes. However, no reliable audiometric hallmarks areavailable for counselling the hearing recovery possibility. Aims/Objectives: Tofind parameters for predicting MD hearing outcomes. Material and Methods: Weapplied machine learning techniques to analyse transient-evoked otoacousticemission (TEOAE) signals recorded from patients with MD. Thirty unilateral MDpatients were recruited prospectively after onset of acute cochleo-vestibularsymptoms. Serial TEOAE and pure-tone audiogram (PTA) data were recordedlongitudinally. Denoised TEOAE signals were projected onto the three mostprominent principal directions through a linear transformation. Binaryclassification was performed using a support vector machine (SVM). TEOAE signalparameters, including signal energy and group delay, were compared betweenimproved and nonimproved groups using Welchs t-test. Results: Signal energy didnot differ (p = 0.64) but a significant difference in 1-kHz (p = 0.045) groupdelay was recorded between improved and nonimproved groups. The SVM achieved across-validated accuracy of >80% in predicting hearing outcomes. Conclusionsand Significance: This study revealed that baseline TEOAE parameters obtainedduring acute MD episodes, when processed through machine learning technology,may provide information on outer hair cell function to predict hearingrecovery.

Transient-evoked otoacoustic emission signals predicting outcomes of acute sensorineural hearing loss in patients with Meniere's Disease