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The emergence of machine learning in auditory neural impairment: A systematic review

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 e...

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Main Authors:	Abu Bakar, Abdul Rauf, Lai, Khin Wee, Hamzaid, Nur Azah
Format:	Article
Published:	Elsevier Ireland Ltd 2021
Subjects:	R Medicine (General) TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/27200/
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spelling	my.um.eprints.272002022-05-31T03:51:30Z http://eprints.um.edu.my/27200/ The emergence of machine learning in auditory neural impairment: A systematic review Abu Bakar, Abdul Rauf Lai, Khin Wee Hamzaid, Nur Azah R Medicine (General) TA Engineering (General). Civil engineering (General) 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. Elsevier Ireland Ltd 2021-11-20 Article PeerReviewed Abu Bakar, Abdul Rauf and Lai, Khin Wee and Hamzaid, Nur Azah (2021) The emergence of machine learning in auditory neural impairment: A systematic review. Neuroscience Letters, 765. ISSN 0304-3940, DOI https://doi.org/10.1016/j.neulet.2021.136250 <https://doi.org/10.1016/j.neulet.2021.136250>. 10.1016/j.neulet.2021.136250
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	R Medicine (General) TA Engineering (General). Civil engineering (General)
spellingShingle	R Medicine (General) TA Engineering (General). Civil engineering (General) Abu Bakar, Abdul Rauf Lai, Khin Wee Hamzaid, Nur Azah The emergence of machine learning in auditory neural impairment: A systematic review
description	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.
format	Article
author	Abu Bakar, Abdul Rauf Lai, Khin Wee Hamzaid, Nur Azah
author_facet	Abu Bakar, Abdul Rauf Lai, Khin Wee Hamzaid, Nur Azah
author_sort	Abu Bakar, Abdul Rauf
title	The emergence of machine learning in auditory neural impairment: A systematic review
title_short	The emergence of machine learning in auditory neural impairment: A systematic review
title_full	The emergence of machine learning in auditory neural impairment: A systematic review
title_fullStr	The emergence of machine learning in auditory neural impairment: A systematic review
title_full_unstemmed	The emergence of machine learning in auditory neural impairment: A systematic review
title_sort	emergence of machine learning in auditory neural impairment: a systematic review
publisher	Elsevier Ireland Ltd
publishDate	2021
url	http://eprints.um.edu.my/27200/
_version_	1735409512165146624
score	13.211869

The emergence of machine learning in auditory neural impairment: A systematic review

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