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

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
Publication Date
2023-03-14
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>
Citation
Physiological Measurement, 44(3), p. 1-20
ISSN
1361-6579
0967-3334
Link
Publisher
Institute of Physics Publishing Ltd
Title
CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals
Type of document
Journal Article
Entity Type
Publication

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