Automated Major Depressive Disorder Classification using Deep Convolutional Neural Networks and Choquet Fuzzy Integral Fusion

Rafiei, A; Wang, Y-K

Automated Major Depressive Disorder Classification using Deep Convolutional Neural Networks and Choquet Fuzzy Integral Fusion

Rafiei, A Wang, Y-K

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, 2022, 00, pp. 186-192
Issue Date:: 2022-12-07

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Field	Value	Language
dc.contributor.author	Rafiei, A
dc.contributor.author	Wang, Y-K
dc.date	2022-12-04
dc.date.accessioned	2023-04-18T03:13:10Z
dc.date.available	2023-04-18T03:13:10Z
dc.date.issued	2022-12-07
dc.identifier.citation	Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, 2022, 00, pp. 186-192
dc.identifier.isbn	9781665487689
dc.identifier.uri	http://hdl.handle.net/10453/169904
dc.description.abstract	Major depressive disorder (MDD) is a common and severe ailment impacting functional frailty, while its concrete manifestations have been shrouded in mystery. Hence, manual diagnosis of MDD is an arduous and subjective task. Despite the aid of electroencephalogram (EEG) signals in the detection, developing intelligent systems are required to improve clinical utility, performance, and efficiency. In this study, we focus on the automated detection of MDD via raw EEG data using convolutional neural networks (CNN). For this objective, we first extracted the short-time Fourier transform (STFT) of EEG records for five distinct band powers and created an image representing the frequency oscillation of every channel during a resting state. Afterward, we applied three approaches to determine whether a subject is MDD or a Healthy individual. In the first approach, a 2D-CNN model was developed for each band power to detect MDD separately. Second, the outcomes of the developed models were used to establish a Choquet fuzzy integral fusion to classify subjects using all of the previous models. The third approach was dedicated to introducing a 3D-CNN architecture. This model received three-dimensional data by putting different band powers' images together. The two last approaches achieved a 95.65% accuracy and 100% sensitivity to detect MDD. The proposed approaches can help clinicians as straightforward, efficient, and intelligent diagnostic tools for detecting MDD.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	Defence Science and Technology Group of the Department of DefenceDSTG
dc.relation	Lockheed Martin Corporation
dc.relation	Defence Science and Technology Group of the Department of Defence
dc.relation.ispartof	Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
dc.relation.ispartof	2022 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.relation.isbasedon	10.1109/ssci51031.2022.10022271
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Automated Major Depressive Disorder Classification using Deep Convolutional Neural Networks and Choquet Fuzzy Integral Fusion
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Arts and Social Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-01-30T00:00:00+1000Z
dc.date.updated	2023-04-18T03:13:08Z
pubs.finish-date	2022-12-07
pubs.publication-status	Published
pubs.start-date	2022-12-04
pubs.volume	00

Abstract:

Major depressive disorder (MDD) is a common and severe ailment impacting functional frailty, while its concrete manifestations have been shrouded in mystery. Hence, manual diagnosis of MDD is an arduous and subjective task. Despite the aid of electroencephalogram (EEG) signals in the detection, developing intelligent systems are required to improve clinical utility, performance, and efficiency. In this study, we focus on the automated detection of MDD via raw EEG data using convolutional neural networks (CNN). For this objective, we first extracted the short-time Fourier transform (STFT) of EEG records for five distinct band powers and created an image representing the frequency oscillation of every channel during a resting state. Afterward, we applied three approaches to determine whether a subject is MDD or a Healthy individual. In the first approach, a 2D-CNN model was developed for each band power to detect MDD separately. Second, the outcomes of the developed models were used to establish a Choquet fuzzy integral fusion to classify subjects using all of the previous models. The third approach was dedicated to introducing a 3D-CNN architecture. This model received three-dimensional data by putting different band powers' images together. The two last approaches achieved a 95.65% accuracy and 100% sensitivity to detect MDD. The proposed approaches can help clinicians as straightforward, efficient, and intelligent diagnostic tools for detecting MDD.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/169904