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Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most...

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Autores principales: Islam, Md Nahidul, Sulaiman, Norizam, Farid, Fahmid Al, Uddin, Jia, Alyami, Salem A., Rashid, Mamunur, P.P. Abdul Majeed, Anwar, Moni, Mohammad Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507488/
https://www.ncbi.nlm.nih.gov/pubmed/34712786
http://dx.doi.org/10.7717/peerj-cs.638
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author Islam, Md Nahidul
Sulaiman, Norizam
Farid, Fahmid Al
Uddin, Jia
Alyami, Salem A.
Rashid, Mamunur
P.P. Abdul Majeed, Anwar
Moni, Mohammad Ali
author_facet Islam, Md Nahidul
Sulaiman, Norizam
Farid, Fahmid Al
Uddin, Jia
Alyami, Salem A.
Rashid, Mamunur
P.P. Abdul Majeed, Anwar
Moni, Mohammad Ali
author_sort Islam, Md Nahidul
collection PubMed
description Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain’s cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method’s performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis.
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spelling pubmed-85074882021-10-27 Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline Islam, Md Nahidul Sulaiman, Norizam Farid, Fahmid Al Uddin, Jia Alyami, Salem A. Rashid, Mamunur P.P. Abdul Majeed, Anwar Moni, Mohammad Ali PeerJ Comput Sci Bioinformatics Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain’s cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method’s performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis. PeerJ Inc. 2021-09-29 /pmc/articles/PMC8507488/ /pubmed/34712786 http://dx.doi.org/10.7717/peerj-cs.638 Text en © 2021 Islam et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Islam, Md Nahidul
Sulaiman, Norizam
Farid, Fahmid Al
Uddin, Jia
Alyami, Salem A.
Rashid, Mamunur
P.P. Abdul Majeed, Anwar
Moni, Mohammad Ali
Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline
title Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline
title_full Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline
title_fullStr Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline
title_full_unstemmed Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline
title_short Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline
title_sort diagnosis of hearing deficiency using eeg based aep signals: cwt and improved-vgg16 pipeline
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507488/
https://www.ncbi.nlm.nih.gov/pubmed/34712786
http://dx.doi.org/10.7717/peerj-cs.638
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