One of the serious mental disorders where people interpret reality in an abnormal state is schizophrenia. A combination of extremely disordered thinking, delusion, and hallucination is caused due to schizophrenia, and the daily functions of a person are severely disturbed because of this disorder. A wide range of problems are caused due to schizophrenia such as disturbed thinking and behaviour. In the field of human neuroscience, the analysis of brain activity is quite an important research area. For general cognitive activity analysis, electroencephalography (EEG) signals are widely used as a low-resolution diagnosis tool. The EEG signals are a great boon to understand the abnormality of the brain disorders, especially schizophrenia. In this work, schizophrenia EEG signal classification is performed wherein, initially, features such as Detrend Fluctuation Analysis (DFA), Hurst Exponent, Recurrence Quantification Analysis (RQA), Sample Entropy, Fractal Dimension (FD), Kolmogorov Complexity, Hjorth exponent, Lempel Ziv Complexity (LZC), and Largest Lyapunov Exponent (LLE) are extracted initially. The extracted features are, then, optimized for selecting the best features through four types of optimization algorithms here such as Artificial Flora (AF) optimization, Glowworm Search (GS) optimization, Black Hole (BH) optimization, and Monkey Search (MS) optimization, and finally, it is classified through certain classifiers. The best results show that, for normal cases, a classification accuracy of 87.54% is obtained when BH optimization is utilized with Support Vector Machine-Radial Basis Function (SVM-RBF) kernel, and for schizophrenia cases, a classification accuracy of 92.17% is obtained when BH optimization is utilized with SVM-RBF kernel.

中文翻译：

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基于群体智能计算的精神分裂症脑电信号分类

人们以异常状态解释现实的严重精神障碍之一是精神分裂症。精神分裂症引起思维，妄想和幻觉的极大紊乱，并且由于这种紊乱，严重影响了人的日常生活。精神分裂症会引起各种各样的问题，例如思维和行为受到干扰。在人类神经科学领域，对大脑活动的分析是相当重要的研究领域。对于一般的认知活动分析，脑电图（EEG）信号被广泛用作低分辨率诊断工具。脑电信号是了解脑部疾病（尤其是精神分裂症）异常的好消息。在这项工作中，进行精神分裂症脑电信号分类，其中，首先，诸如趋势波动分析（DFA），赫斯特指数，递归定量分析（RQA），样本熵，分形维（FD），柯尔莫哥洛夫复杂度，霍耳斯指数，Lempel Ziv复杂度（LZC）和最大Lyapunov指数（LLE）等功能最初提取。然后，通过此处的四种优化算法对提取的特征进行优化，以选择最佳特征，例如人工植物区系（AF）优化，萤火虫搜索（GS）优化，黑洞（BH）优化和猴子搜索（MS）优化，最后，它通过某些分类器进行分类。最佳结果表明，在正常情况下，将支持向量机-径向基函数（SVM-RBF）内核与BH优化结合使用时，对于精神分裂症，分类精度为87.54％，