Major Depressive Disorder Detection based on Parallel Spatiotemporal Convolution Network

In recent years, the number of patients with depression has grown rapidly. The traditional diagnosis of depression includes mental scales, clinical inquiry etc., which is time consuming and lacks objective confirmation of relevant physiology indicators. In order to overcome the drawback of traditional methods, brain imaging techniques such as electroencephalogram (EEG) have provided new tools for diagnosing depression and shown excellent performance. In this paper, a major depressive disorder (MDD) detection framework is proposed based on parallel spatiotemporal convolution network and mix-multilayer perceptron. First, the wavelet entropy and differential entropy features of EEG were extracted and then parallel spatial temporal convolutional network and mix-multilayer perceptron were employed for further feature representation and extraction. In this process, mmd-loss was creatively added to shorten the gap between the training dataset and the test dataset. Further extracted features were fused and multilayer-perceptron (MLP) was used to perform binary classification. This experiment was evaluated on the MODMA dataset and achieved an accuracy of 0.7832. The experimental results show that the model proposed in our paper is effective in MDD detection and provides better performance compared with the baseline systems.