Identification of Depression with a Semi-supervised GCN based on EEG Data

Abstract

In this paper, to explore the application of depression EEG data in semi-supervised classification, we designed an improved semi-supervised graph convolutional neural network model for depression identification. Fifty-three volunteers, including 24 patients with depression and 29 healthy controls, participated in the study. Electroencephalogram (EEG) data from 128 channels were recorded in the resting state for 5 minutes. The differential entropy feature of EEG is obtained and its Pearson matrix is used to construct the node and adjacency matrix of the graph. For the classifier, we combined self-organizing incremental neural network (SOINN) and graph convolutional neural network (GCN) self-training to expand the training set, improve the effect of classification, and adopted 10-fold cross validation to verify the classification results. Compared with convolutional neural networks (CNN), long short-term memory (LSTM) neural network and classical fully supervised algorithms, such as support vector machine (SVM), the classification accuracy of the proposed model is 70.53% and 92.23% under the condition of label data of 50 and 600, respectively. Compared with the original GCN model, our method has significantly improved the performance indicators of Accuracy (Acc), Recall (Rec), Specificity (Spec) and Precision (Pre) and improved the ability of GCN label propagation. This study provides a semi-supervised learning model for detecting depression and a new method for diagnosing depression based on EEG signals.