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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| Format: | Article |
| Language: | English |
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Peerj Inc.
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/32669/ http://umpir.ump.edu.my/id/eprint/32669/1/Diagnosis%20of%20hearing%20deficiency%20using%20EEG%20based%20AEP%20signals.pdf |
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| author | Islam, Md Nahidul Norizam, Sulaiman Al Farid, Fahmid Uddin, Jia Alyami, Salem A. Rashid, Mamunur Abdul Majeed, Anwar P.P. Moni, Mohammad Ali |
| author_facet | Islam, Md Nahidul Norizam, Sulaiman Al Farid, Fahmid Uddin, Jia Alyami, Salem A. Rashid, Mamunur Abdul Majeed, Anwar P.P. Moni, Mohammad Ali |
| author_sort | Islam, Md Nahidul |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:07:21Z |
| format | Article |
| id | ump-32669 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:07:21Z |
| publishDate | 2021 |
| publisher | Peerj Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-326692022-02-07T04:09:07Z http://umpir.ump.edu.my/id/eprint/32669/ Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline Islam, Md Nahidul Norizam, Sulaiman Al Farid, Fahmid Uddin, Jia Alyami, Salem A. Rashid, Mamunur Abdul Majeed, Anwar P.P. Moni, Mohammad Ali TK Electrical engineering. Electronics Nuclear engineering 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 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/32669/1/Diagnosis%20of%20hearing%20deficiency%20using%20EEG%20based%20AEP%20signals.pdf Islam, Md Nahidul and Norizam, Sulaiman and Al Farid, Fahmid and Uddin, Jia and Alyami, Salem A. and Rashid, Mamunur and Abdul Majeed, Anwar P.P. and Moni, Mohammad Ali (2021) Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline. PeerJ Computer Science, 7. pp. 1-28. ISSN 2376-5992. (Published) https://doi.org/10.7717/peerj-cs.638 https://doi.org/10.7717/peerj-cs.638 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Islam, Md Nahidul Norizam, Sulaiman Al Farid, Fahmid Uddin, Jia Alyami, Salem A. Rashid, Mamunur Abdul Majeed, Anwar P.P. 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 | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/32669/ http://umpir.ump.edu.my/id/eprint/32669/ http://umpir.ump.edu.my/id/eprint/32669/ http://umpir.ump.edu.my/id/eprint/32669/1/Diagnosis%20of%20hearing%20deficiency%20using%20EEG%20based%20AEP%20signals.pdf |