CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals.

Baygin, M; Barua, PD; Chakraborty, S; Tuncer, I; Dogan, S; Palmer, E; Tuncer, T; Kamath, AP; Ciaccio, EJ; Acharya, UR

CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals.

Baygin, M Barua, PD Chakraborty, S

Tuncer, I Dogan, S Palmer, E Tuncer, T Kamath, AP Ciaccio, EJ Acharya, UR

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Publisher:: IOP Publishing Ltd
Publication Type:: Journal Article
Citation:: Physiol Meas, 2023, 44, (3)
Issue Date:: 2023-03-14

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Field	Value	Language
dc.contributor.author	Baygin, M
dc.contributor.author	Barua, PD
dc.contributor.author	Chakraborty, S https://orcid.org/0000-0002-0102-5424
dc.contributor.author	Tuncer, I
dc.contributor.author	Dogan, S
dc.contributor.author	Palmer, E
dc.contributor.author	Tuncer, T
dc.contributor.author	Kamath, AP
dc.contributor.author	Ciaccio, EJ
dc.contributor.author	Acharya, UR
dc.date.accessioned	2024-02-27T06:11:09Z
dc.date.available	2023-01-04
dc.date.available	2024-02-27T06:11:09Z
dc.date.issued	2023-03-14
dc.identifier.citation	Physiol Meas, 2023, 44, (3)
dc.identifier.issn	0967-3334
dc.identifier.issn	1361-6579
dc.identifier.uri	http://hdl.handle.net/10453/175898
dc.description.abstract	Objective.Schizophrenia (SZ) is a severe, chronic psychiatric-cognitive disorder. The primary objective of this work is to present a handcrafted model using state-of-the-art technique to detect SZ accurately with EEG signals.Approach.In our proposed work, the features are generated using a histogram-based generator and an iterative decomposition model. The graph-based molecular structure of the carbon chain is employed to generate low-level features. Hence, the developed feature generation model is called the carbon chain pattern (CCP). An iterative tunable q-factor wavelet transform (ITQWT) technique is implemented in the feature extraction phase to generate various sub-bands of the EEG signal. The CCP was applied to the generated sub-bands to obtain several feature vectors. The clinically significant features were selected using iterative neighborhood component analysis (INCA). The selected features were then classified using the k nearest neighbor (kNN) with a 10-fold cross-validation strategy. Finally, the iterative weighted majority method was used to obtain the results in multiple channels.Main results.The presented CCP-ITQWT and INCA-based automated model achieved an accuracy of 95.84% and 99.20% using a single channel and majority voting method, respectively with kNN classifier.Significance.Our results highlight the success of the proposed CCP-ITQWT and INCA-based model in the automated detection of SZ using EEG signals.
dc.format	Electronic
dc.language	eng
dc.publisher	IOP Publishing Ltd
dc.relation.ispartof	Physiol Meas
dc.relation.isbasedon	10.1088/1361-6579/acb03c
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering, 1116 Medical Physiology
dc.subject.classification	Biomedical Engineering
dc.subject.classification	3208 Medical physiology
dc.subject.classification	4003 Biomedical engineering
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Schizophrenia
dc.subject.mesh	Wavelet Analysis
dc.subject.mesh	Cognitive Dysfunction
dc.subject.mesh	Carbon
dc.subject.mesh	Algorithms
dc.subject.mesh	Humans
dc.subject.mesh	Carbon
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Schizophrenia
dc.subject.mesh	Algorithms
dc.subject.mesh	Wavelet Analysis
dc.subject.mesh	Cognitive Dysfunction
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Schizophrenia
dc.subject.mesh	Wavelet Analysis
dc.subject.mesh	Cognitive Dysfunction
dc.subject.mesh	Carbon
dc.subject.mesh	Algorithms
dc.title	CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals.
dc.type	Journal Article
utslib.citation.volume	44
utslib.location.activity	England
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1116 Medical Physiology
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
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access	*
dc.date.updated	2024-02-27T06:11:08Z
pubs.issue	3
pubs.publication-status	Published online
pubs.volume	44
utslib.citation.issue	3

Abstract:

Objective.Schizophrenia (SZ) is a severe, chronic psychiatric-cognitive disorder. The primary objective of this work is to present a handcrafted model using state-of-the-art technique to detect SZ accurately with EEG signals.Approach.In our proposed work, the features are generated using a histogram-based generator and an iterative decomposition model. The graph-based molecular structure of the carbon chain is employed to generate low-level features. Hence, the developed feature generation model is called the carbon chain pattern (CCP). An iterative tunable q-factor wavelet transform (ITQWT) technique is implemented in the feature extraction phase to generate various sub-bands of the EEG signal. The CCP was applied to the generated sub-bands to obtain several feature vectors. The clinically significant features were selected using iterative neighborhood component analysis (INCA). The selected features were then classified using the k nearest neighbor (kNN) with a 10-fold cross-validation strategy. Finally, the iterative weighted majority method was used to obtain the results in multiple channels.Main results.The presented CCP-ITQWT and INCA-based automated model achieved an accuracy of 95.84% and 99.20% using a single channel and majority voting method, respectively with kNN classifier.Significance.Our results highlight the success of the proposed CCP-ITQWT and INCA-based model in the automated detection of SZ using EEG signals.

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http://hdl.handle.net/10453/175898