CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals
Article
Baygin, Mehmet, Barua, Prabal Datta, Chakraborty, Subrata, Tuncer, Ilknur, Dogan, Sengul, Palmer, Elizabeth, Tuncer, Turker, Kamath, Aditya, Ciaccio, Edward J and Acharya, U Rajendra. 2023. "CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals
." Physiological Measurement. 44 (3). https://doi.org/10.1088/1361-6579/acb03c
| Article Title | CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals |
|---|---|
| ERA Journal ID | 14630 |
| Article Category | Article |
| Authors | Baygin, Mehmet, Barua, Prabal Datta, Chakraborty, Subrata, Tuncer, Ilknur, Dogan, Sengul, Palmer, Elizabeth, Tuncer, Turker, Kamath, Aditya, Ciaccio, Edward J and Acharya, U Rajendra |
| Journal Title | Physiological Measurement |
| Journal Citation | 44 (3) |
| Article Number | 035008 |
| Number of Pages | 20 |
| Year | 2023 |
| Publisher | IOP Publishing |
| Place of Publication | Netherlands |
| ISSN | 0967-3334 |
| 1361-6579 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1088/1361-6579/acb03c |
| Web Address (URL) | https://iopscience.iop.org/article/10.1088/1361-6579/acb03c |
| 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. |
| Keywords | carbon chain pattern |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Ardahan University, Turkiye |
| School of Management and Enterprise | |
| University of Technology Sydney | |
| University of New England | |
| Interior Ministry, Turkiye | |
| Firat University, Turkey | |
| Sydney Children's Hospital, Australia | |
| University of New South Wales | |
| Brown University, United States | |
| Columbia University Irving Medical Center, United States | |
| Ngee Ann Polytechnic, Singapore | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan |
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