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A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline

The auditory evoked potential (AEP) has been considered a standard clinical instrument for hearing and neurological evaluation. Although several approaches for learning EEG signal characteristics have been established earlier, the hybridization concept has rarely been explored to produce novel repre...

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Main Authors: Islam, Md Nahidul, Norizam, Sulaiman, Bari, Bifta Sama, Rashid, Mamunur, Mahfuzah, Mustafa
Format: Article
Language:English
Published: Elsevier 2022
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/33319/1/A%20hybrid%20scheme%20for%20AEP%20based%20hearing.pdf
http://umpir.ump.edu.my/id/eprint/33319/
https://doi.org/10.1016/j.neuri.2021.100037
https://doi.org/10.1016/j.neuri.2021.100037
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spelling my.ump.umpir.333192022-02-07T04:51:54Z http://umpir.ump.edu.my/id/eprint/33319/ A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline Islam, Md Nahidul Norizam, Sulaiman Bari, Bifta Sama Rashid, Mamunur Mahfuzah, Mustafa TK Electrical engineering. Electronics Nuclear engineering The auditory evoked potential (AEP) has been considered a standard clinical instrument for hearing and neurological evaluation. Although several approaches for learning EEG signal characteristics have been established earlier, the hybridization concept has rarely been explored to produce novel representations of AEP features and achieve further performance enhancement for AEP signals. Moreover, the classification of auditory attention within a concise time interval is still facing some challenges. To address this concern, this study has proposed a hybridization scheme, represented as a hybrid convoluted k-nearest neighbour (CKNN) algorithm, consisting of concatenating the convolutional layer of CNN with k-nearest neighbour (k-NN) classifier. The proposed architecture helps in improving the accuracy of KNN from 83.23% to 92.26% with a 3-second decision window. The effect of several concise decision windows is also investigated in this analysis. The proposed architecture is validated by a publicly benchmark AEP dataset, and the outcomes indicate that the CKNN significantly outperforms other state-of-the-art techniques with a concise decision window. The proposed framework shows superior performance in a concise decision window that can be effectively used for early hearing deficiency diagnosis. This paper also presents several discoveries that could be helpful to the neurological community. Elsevier 2022 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/33319/1/A%20hybrid%20scheme%20for%20AEP%20based%20hearing.pdf Islam, Md Nahidul and Norizam, Sulaiman and Bari, Bifta Sama and Rashid, Mamunur and Mahfuzah, Mustafa (2022) A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline. Neuroscience Informatics, 2 (1). pp. 1-13. ISSN 2772-5286 https://doi.org/10.1016/j.neuri.2021.100037 https://doi.org/10.1016/j.neuri.2021.100037
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Islam, Md Nahidul
Norizam, Sulaiman
Bari, Bifta Sama
Rashid, Mamunur
Mahfuzah, Mustafa
A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline
description The auditory evoked potential (AEP) has been considered a standard clinical instrument for hearing and neurological evaluation. Although several approaches for learning EEG signal characteristics have been established earlier, the hybridization concept has rarely been explored to produce novel representations of AEP features and achieve further performance enhancement for AEP signals. Moreover, the classification of auditory attention within a concise time interval is still facing some challenges. To address this concern, this study has proposed a hybridization scheme, represented as a hybrid convoluted k-nearest neighbour (CKNN) algorithm, consisting of concatenating the convolutional layer of CNN with k-nearest neighbour (k-NN) classifier. The proposed architecture helps in improving the accuracy of KNN from 83.23% to 92.26% with a 3-second decision window. The effect of several concise decision windows is also investigated in this analysis. The proposed architecture is validated by a publicly benchmark AEP dataset, and the outcomes indicate that the CKNN significantly outperforms other state-of-the-art techniques with a concise decision window. The proposed framework shows superior performance in a concise decision window that can be effectively used for early hearing deficiency diagnosis. This paper also presents several discoveries that could be helpful to the neurological community.
format Article
author Islam, Md Nahidul
Norizam, Sulaiman
Bari, Bifta Sama
Rashid, Mamunur
Mahfuzah, Mustafa
author_facet Islam, Md Nahidul
Norizam, Sulaiman
Bari, Bifta Sama
Rashid, Mamunur
Mahfuzah, Mustafa
author_sort Islam, Md Nahidul
title A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline
title_short A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline
title_full A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline
title_fullStr A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline
title_full_unstemmed A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline
title_sort hybrid scheme for aep based hearing deficiency diagnosis: cwt and convoluted k-nearest neighbour (cknn) pipeline
publisher Elsevier
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/33319/1/A%20hybrid%20scheme%20for%20AEP%20based%20hearing.pdf
http://umpir.ump.edu.my/id/eprint/33319/
https://doi.org/10.1016/j.neuri.2021.100037
https://doi.org/10.1016/j.neuri.2021.100037
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score 13.211869

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