CGP17Pat: Automated Schizophrenia Detection Based on a Cyclic Group of Prime Order Patterns Using EEG Signals

Emrah Aydemir; Sengul Dogan; Mehmet Baygin; Chui Ping Ooi; Prabal Datta Barua; Turker Tuncer; U Rajendra Acharya

doi:10.3390/healthcare10040643

CGP17Pat: Automated Schizophrenia Detection Based on a Cyclic Group of Prime Order Patterns Using EEG Signals

Healthcare (Basel). 2022 Mar 29;10(4):643. doi: 10.3390/healthcare10040643.

Authors

Emrah Aydemir¹, Sengul Dogan², Mehmet Baygin³, Chui Ping Ooi⁴, Prabal Datta Barua^{5

6}, Turker Tuncer², U Rajendra Acharya^{7

8

9}

Affiliations

¹ Department of Management Information Systems, Management Faculty, Sakarya University, Sakarya 54050, Turkey.
² Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey.
³ Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan 75000, Turkey.
⁴ School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore.
⁵ School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350, Australia.
⁶ Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
⁷ Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Singapore 599489, Singapore.
⁸ Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599491, Singapore.
⁹ Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 413, Taiwan.

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: EEG classification; NCA; cyclic group of prime order pattern; kNN; machine learning; schizophrenia detection.