Aydemir Emrah, Dogan Sengul, Baygin Mehmet, Ooi Chui Ping, Barua Prabal Datta, Tuncer Turker, Acharya U Rajendra
Department of Management Information Systems, Management Faculty, Sakarya University, Sakarya 54050, Turkey.
Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey.
Healthcare (Basel). 2022 Mar 29;10(4):643. doi: 10.3390/healthcare10040643.
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.
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.
The presented CGP17Pat-based EEG classification model attained 99.91% accuracy employing 10-fold cross-validation and 84.33% accuracy using the LOSO strategy.
The findings and results depicted the high classification ability of the presented cryptologic pattern for the data set used.
机器学习模型已被用于诊断精神分裂症。本研究的主要目的是介绍一种有效的精神分裂症手工建模分类方法。
本研究使用了一个公开的脑电图（EEG）信号数据集，并提出了一种使用模17运算的素数阶循环群的自动精神分裂症检测模型。因此，所提出的特征提取器被命名为素数阶循环群模式，即CGP17Pat。利用所提出的CGP17Pat，提出了一种新的多级特征提取模型。为了选择高度独特的特征，使用了迭代邻域成分分析（INCA），并使用k近邻（kNN）结合10折交叉验证和留一受试者出（LOSO）验证技术对这些特征进行分类。最后，在最后阶段采用迭代硬多数投票来获得通道级结果，并计算总体结果。
所提出的基于CGP17Pat的EEG分类模型在10折交叉验证下准确率达到99.91%，在LOSO策略下准确率达到84.33%。
研究结果表明了所提出的密码学模式对所用数据集具有较高的分类能力。
