The emergence of machine learning in auditory neural impairment: A systematic review
Document Type
Article
Publication Date
11-20-2021
Abstract
Hearing loss is a common neurodegenerative disease that can start at any stage of life. Misalignment of the auditory neural impairment may impose challenges in processing incoming auditory stimulus that can be measured using electroencephalography (EEG). The electrophysiological behaviour response emanated from EEG auditory evoked potential (AEP) requires highly trained professionals for analysis and interpretation. Reliable automated methods using techniques of machine learning would assist the auditory assessment process for informed treatment and practice. It is thus highly required to develop models that are more efficient and precise by considering the characteristics of brain signals. This study aims to provide a comprehensive review of several state-of-the-art techniques of machine learning that adopt EEG evoked response for the auditory assessment within the last 13 years. Out of 161 initially screened articles, 11 were retained for synthesis. The outcome of the review presented that the Support Vector Machine (SVM) classifier outperformed with over 80% accuracy metric and was recognized as the best suited model within the field of auditory research. This paper discussed the comprehensive iterative properties of the proposed computed algorithms and the feasible future direction in hearing impaired rehabilitation.
Keywords
Machine learning, Classification, Auditory Evoked Potential (AEP), Electroencephalography (EEG), Auditory assessment
Divisions
fac_eng
Funders
Ministry of Education, Malaysia,Universiti Malaya (FRGS/1/2019/TK04/UM/01/2),Malaysia Research University Network grant (MRUN2020-1A),Public Service Department of Malaysia (JPA)
Publication Title
Neuroscience Letters
Volume
765
Publisher
Elsevier Ireland Ltd
Publisher Location
ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND