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
- Publisher:
- IOP Publishing Ltd
- Publication Type:
- Journal Article
- Citation:
- Physiol Meas, 2023, 44, (3)
- Issue Date:
- 2023-03-14
Closed Access
Filename | Description | Size | |||
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Baygin_2023_Physiol._Meas._44_035008.pdf | Published version | 1.5 MB | Adobe PDF |
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Full metadata record
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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