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

Healthcare

Mehmet Baygin¹  Sengul Dogan²  Turker Tuncer²  Emrah Aydemir³  U. Rajendra Acharya⁴  Prabal Datta Barua⁵  Chui Ping Ooi⁶ 

[1] Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan 75000, Turkey;Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey;Department of Management Information Systems, Management Faculty, Sakarya University, Sakarya 54050, Turkey;Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Singapore 599489, Singapore;School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350, Australia;School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;

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

DOI  :  10.3390/healthcare10040643

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown