The Diagnosis of Major Depressive Disorder Through Wearable fNIRS by Using Wavelet Transform and Parallel-CNN Feature Fusion

Wang, G; Wu, N; Tao, Y; Lee, WH; Cao, Z; Yan, X; Wang, G

The Diagnosis of Major Depressive Disorder Through Wearable fNIRS by Using Wavelet Transform and Parallel-CNN Feature Fusion

Wang, G Wu, N Tao, Y Lee, WH Cao, Z

Yan, X Wang, G

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Instrumentation and Measurement, 2023, 72
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Wang, G
dc.contributor.author	Wu, N
dc.contributor.author	Tao, Y
dc.contributor.author	Lee, WH
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.contributor.author	Yan, X
dc.contributor.author	Wang, G
dc.date.accessioned	2024-03-26T04:13:43Z
dc.date.available	2024-03-26T04:13:43Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Instrumentation and Measurement, 2023, 72
dc.identifier.issn	0018-9456
dc.identifier.issn	1557-9662
dc.identifier.uri	http://hdl.handle.net/10453/177170
dc.description.abstract	Depression is a common mental illness that can even lead to suicide in severe cases. Thus, it is essential to diagnose and duly treat the depressive disorder accurately. Functional near-infrared spectroscopy (fNIRS) signals can monitor cerebral hemodynamic activity and may serve as a biomarker of depression. In this study, using wavelet transform and parallel convolutional neural network (CNN) feature fusion (WPCF), a novel algorithm based on a few channels of fNIRS signals was proposed to diagnose depressive disorder. First, the preprocessed fNIRS signals were transformed into 2-D wavelet feature maps. Second, the feature maps with best quality were selected to form a feature map subset. Finally, the feature map subset was used as an input into the WPCF algorithm for discriminating between the patients with major depressive disorder (MDD) and the healthy subjects. When using the subjectwise split data, the WPCF achieved good performance with an accuracy of 89.1% in the posttask resting state. For recordwise split data, the results attained by the proposed WPCF algorithm had an accuracy of 95.4%. These results indicated that the WPCF algorithm based on fNIRS signals may be applied to the home environment due to the portability and noninvasive measurement of the wearable fNIRS instrument.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Instrumentation and Measurement
dc.relation.isbasedon	10.1109/TIM.2023.3303233
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0299 Other Physical Sciences, 0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	The Diagnosis of Major Depressive Disorder Through Wearable fNIRS by Using Wavelet Transform and Parallel-CNN Feature Fusion
dc.type	Journal Article
utslib.citation.volume	72
utslib.for	0299 Other Physical Sciences
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-26T04:13:42Z
pubs.publication-status	Published
pubs.volume	72

Abstract:

Depression is a common mental illness that can even lead to suicide in severe cases. Thus, it is essential to diagnose and duly treat the depressive disorder accurately. Functional near-infrared spectroscopy (fNIRS) signals can monitor cerebral hemodynamic activity and may serve as a biomarker of depression. In this study, using wavelet transform and parallel convolutional neural network (CNN) feature fusion (WPCF), a novel algorithm based on a few channels of fNIRS signals was proposed to diagnose depressive disorder. First, the preprocessed fNIRS signals were transformed into 2-D wavelet feature maps. Second, the feature maps with best quality were selected to form a feature map subset. Finally, the feature map subset was used as an input into the WPCF algorithm for discriminating between the patients with major depressive disorder (MDD) and the healthy subjects. When using the subjectwise split data, the WPCF achieved good performance with an accuracy of 89.1% in the posttask resting state. For recordwise split data, the results attained by the proposed WPCF algorithm had an accuracy of 95.4%. These results indicated that the WPCF algorithm based on fNIRS signals may be applied to the home environment due to the portability and noninvasive measurement of the wearable fNIRS instrument.

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http://hdl.handle.net/10453/177170