Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade

Khare, SK; March, S; Barua, PD; Gadre, VM; Acharya, UR

Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade

Khare, SK March, S Barua, PD Gadre, VM Acharya, UR

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Information Fusion, 2023, 99, pp. 101898
Issue Date:: 2023-11-01

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Field	Value	Language
dc.contributor.author	Khare, SK
dc.contributor.author	March, S
dc.contributor.author	Barua, PD
dc.contributor.author	Gadre, VM
dc.contributor.author	Acharya, UR
dc.date.accessioned	2024-03-31T22:01:53Z
dc.date.available	2024-03-31T22:01:53Z
dc.date.issued	2023-11-01
dc.identifier.citation	Information Fusion, 2023, 99, pp. 101898
dc.identifier.issn	1566-2535
dc.identifier.uri	http://hdl.handle.net/10453/177382
dc.description.abstract	Mental health is a basic need for a sustainable and developing society. The prevalence and financial burden of mental illness have increased globally, and especially in response to community and worldwide pandemic events. Children suffering from such mental disorders find it difficult to cope with educational, occupational, personal, and societal developments, and treatments are not accessible to all. Advancements in technology have resulted in much research examining the use of artificial intelligence to detect or identify characteristics of mental illness. Therefore, this paper presents a systematic review of nine developmental and mental disorders (Autism spectrum disorder, Attention deficit hyperactivity disorder, Schizophrenia, Anxiety, Depression, Dyslexia, Post-traumatic stress disorder, Tourette syndrome, and Obsessive–compulsive disorder) prominent in children and adolescents. Our paper focuses on the automated detection of these developmental and mental disorders using physiological signals. This paper also presents a detailed discussion on signal analysis, feature engineering, and decision-making with their advantages, future directions and challenges on the papers published on mental disorders of children. We have presented the details of the dataset description, validation techniques, features extracted and decision-making models. The challenges and future directions present open research questions on signal or availability, uncertainty, explainability, and hardware implementation resources for signal analysis and machine or deep learning models. Finally, the main findings of this study are presented in the conclusion section.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Information Fusion
dc.relation.isbasedon	10.1016/j.inffus.2023.101898
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4605 Data management and data science
dc.title	Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade
dc.type	Journal Article
utslib.citation.volume	99
utslib.for	0801 Artificial Intelligence and Image Processing
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 Civil and Environmental Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-31T22:01:49Z
pubs.publication-status	Published
pubs.volume	99

Abstract:

Mental health is a basic need for a sustainable and developing society. The prevalence and financial burden of mental illness have increased globally, and especially in response to community and worldwide pandemic events. Children suffering from such mental disorders find it difficult to cope with educational, occupational, personal, and societal developments, and treatments are not accessible to all. Advancements in technology have resulted in much research examining the use of artificial intelligence to detect or identify characteristics of mental illness. Therefore, this paper presents a systematic review of nine developmental and mental disorders (Autism spectrum disorder, Attention deficit hyperactivity disorder, Schizophrenia, Anxiety, Depression, Dyslexia, Post-traumatic stress disorder, Tourette syndrome, and Obsessive–compulsive disorder) prominent in children and adolescents. Our paper focuses on the automated detection of these developmental and mental disorders using physiological signals. This paper also presents a detailed discussion on signal analysis, feature engineering, and decision-making with their advantages, future directions and challenges on the papers published on mental disorders of children. We have presented the details of the dataset description, validation techniques, features extracted and decision-making models. The challenges and future directions present open research questions on signal or availability, uncertainty, explainability, and hardware implementation resources for signal analysis and machine or deep learning models. Finally, the main findings of this study are presented in the conclusion section.

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http://hdl.handle.net/10453/177382