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

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)

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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.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Electroencephalogram; Deep learning; Auditory Evoked potential; Transfer learning; VGG16
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Institute of Postgraduate Studies
Faculty of Electrical and Electronic Engineering Technology
Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 29 Dec 2021 02:11
Last Modified: 07 Feb 2022 04:09
URI: http://umpir.ump.edu.my/id/eprint/32669
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