关键词: 精神分裂症, 模糊熵, 卷积神经网络, 分类

Abstract: Schizophrenia is a common serious mental disease, which has seriously affected the quality of human life for many years. Therefore, accurate diagnosis is a prerequisite for the treatment of the disease. To solve the problem, this paper proposes a classification method of schizophrenia electroencephalogram（EEG） signals based on brain complexity and deep learning, aiming at discovering hidden distributed features in data. Contrary to the standard EEG data analysis technology that ignores spatial information, the time series of EEG signals are firstly divided into different frequencies, and fuzzy entropy（FuzzyEn） is used to extract the complexity features of each frequency band, then the feature vectors are constructed according to the spatial position of the electrode and input into the Convolution Neural Network（CNN） to train the classification model, so as to automatically identify whether the subject is diseased. The results show that the experimental method is effective, and the classification accuracy reaches 99.16%.

Key words: schizophrenia, fuzzy entropy, Convolution Neural Network（CNN）, classification