关键词: 卷积神经网络（CNN）, 精神分裂症, 抑郁症, 静息态脑电, 数据增强

Abstract:

As clinical complex neuropsychiatric syndromes, schizophrenia and depression often have some similar clinical manifestations. A kind of fast and objective method of distinguishing them is needed. EEG, as a high time resolution and non-invasive method, which can directly detect and track dynamics of brain electrical activities, used as a potential bio-marker for distinguishing two disorders. It classifies the two disorders by training a Convolutional Neural Network（CNN） model using their rest state EEG. Firstly, the rest EEG spectrums are converted to gray scale images as input into the model. Secondly, the CNN model is used to extract features automatically and classify disorders. Thirdly, it uses cross validation to evaluate the model. Finally, data augmentation is applied to EEG data to improve the model performance. The classification accuracy, sensitivity and specificity of the model can reach up to 87.50%, 84.09% and 91.67%, respectively. The results show that original resting state EEG spectrums can be used, without manual feature selection, to distinguish schizophrenia and depression with high accuracy. EEG may be a promising bio-marker for distinguishing them. This study may provide reference value for the clinical diagnose between the two disorders.

Key words: Convolutional Neural Network（CNN）, schizophrenia, depression, rest state EEG, data augmentation