CGP17Pat: Automated Schizophrenia Detection Based on a Cyclic Group of Prime Order Patterns Using EEG Signals
Article
Article Title | CGP17Pat: Automated Schizophrenia Detection Based on a Cyclic Group of Prime Order Patterns Using EEG Signals |
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ERA Journal ID | 212680 |
Article Category | Article |
Authors | Aydemir, Emrah, Dogan, Sengul, Baygin, Mehmet, Ooi, Chui Ping, Barua, Prabal Datta, Tuncer, Turker and Acharya, U. Rajendra |
Journal Title | Healthcare |
Journal Citation | 10 (4), pp. 1-18 |
Article Number | 643 |
Number of Pages | 18 |
Year | 2022 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2227-9032 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/healthcare10040643 |
Web Address (URL) | https://www.mdpi.com/2227-9032/10/4/643 |
Abstract | Background and Purpose: Machine learning models have been used to diagnose schizophrenia. The main purpose of this research is to introduce an effective schizophrenia hand-modeled classification method. Method: A public electroencephalogram (EEG) signal data set was used in this work, and an automated schizophrenia detection model is presented using a cyclic group of prime order with a modulo 17 operator. Therefore, the presented feature extractor was named as the cyclic group of prime order pattern, CGP17Pat. Using the proposed CGP17Pat, a new multilevel feature extraction model is presented. To choose a highly distinctive feature, iterative neighborhood component analysis (INCA) was used, and these features were classified using k-nearest neighbors (kNN) with the 10-fold cross-validation and leave-one-subject-out (LOSO) validation techniques. Finally, iterative hard majority voting was employed in the last phase to obtain channel-wise results, and the general results were calculated. Results: The presented CGP17Pat-based EEG classification model attained 99.91% accuracy employing 10-fold cross-validation and 84.33% accuracy using the LOSO strategy. Conclusions: The findings and results depicted the high classification ability of the presented cryptologic pattern for the data set used. |
Keywords | cyclic group of prime order pattern; EEG classification; kNN; machine learning; NCA; schizophrenia detection |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | Ngee Ann Polytechnic, Singapore |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan | |
Sakarya University, Turkey | |
Firat University, Turkey | |
Ardahan University, Turkiye | |
School of Business | |
University of Technology Sydney |
https://research.usq.edu.au/item/yyw6w/cgp17pat-automated-schizophrenia-detection-based-on-a-cyclic-group-of-prime-order-patterns-using-eeg-signals
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