Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (22): 239-244.

### Research on EEG Classification of Schizophrenia Based on Information Entropy of Functional Connection

LI Peizhen, WANG Bin, NIU Yan, TIAN Cheng, XIANG Jie

1. College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
• Online:2019-11-15 Published:2019-11-13

### 基于功能连接信息熵的精神分裂症EEG分类研究

1. 太原理工大学 信息与计算机学院，太原 030024

Abstract: In order to improve the effective diagnosis of schizophrenia, this paper uses the method of network functional connection information entropy to classify the Electroencephalogram（EEG） of 51 patients with schizophrenia and 56 age-matched normal subjects. This paper achieves an effective diagnosis of schizophrenia by using the methods of frequency division, phase synchronization analysis, information entropy and Support Vector Machine（SVM）, and greatly improves the classification accuracy. The classification method mainly involves two stages. First, the frequency division technique and the phase synchronization analysis method are used to obtain the functional connection matrix of the EEG signals in each frequency band at each time point. Second, the information entropy of each frequency band is calculated based on the functional connections over the entire time domain. And, the information entropy of functional connectivity is used as the classification feature of the functional brain network to train the SVM classifier, then the two groups of subjects are classified. The classification results show that the method proposed in this paper greatly improves the detection accuracy of schizophrenia.