Author(s) |
Baygin, Mehmet
Barua, Prabal Datta
Chakraborty, Subrata
Tuncer, Ilknur
Dogan, Sengul
Palmer, Elizabeth
Tuncer, Turker
Kamath, Aditya P
Ciaccio, Edward J
Rajendra, Acharya U
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Publication Date |
2023-03-14
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Abstract |
<p><i>Objective</i> . 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. <i>Approach</i> . 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. <i>Main results</i> . 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. <i>Significance</i> . Our results highlight the success of the proposed CCP-ITQWT and INCA-based model in the automated detection of SZ using EEG signals.</p>
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Citation |
Physiological Measurement, 44(3), p. 1-20
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ISSN |
1361-6579
0967-3334
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Link | |
Publisher |
Institute of Physics Publishing Ltd
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Title |
CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals
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Type of document |
Journal Article
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Entity Type |
Publication
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