Transient-evoked otoacoustic emission signals predicting outcomes of acute sensorineural hearing loss in patients with Ménière's disease. (3rd March 2020)

• Record Type: Journal Article
• Authors: Liu, Yi-Wen; Kao, Sheng-Lun; Wu, Hau-Tieng; Liu, Tzu-Chi; Fang, Te-Yung; Wang, Pa-Chun
• Is Part Of: Acta oto-laryngologica. Volume 140:Number 3(2020)
• Page Start: 230
• Page End: 235
• Publication Date: 2020-03-03
• Subjects: Transient-evoked otoacoustic emission -- acute sensorineural hearing loss -- Ménière's disease -- machine learning; Otolaryngology -- Periodicals; Ear -- Diseases -- Periodicals; Throat -- Diseases -- Periodicals; Otolaryngology -- Electronic Resources; Otorhinolaryngologic Diseases; 617.8
• DOI: 10.1080/00016489.2019.1704865
• Languages: English
• ISSNs: 0001-6489

Abstract

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.