Multilayer brain network combined with deep convolutional neural network for detecting major depressive disorder

Abstract

As a global and grievous mental disease, major depressive disorder (MDD) has received much attention. Accurate detection of MDD via physiological signals represents an urgent research topic. Here, a frequency-dependent multilayer brain network, combined with deep convolutional neural network (CNN), is developed to detect the MDD. Multivariate pseudo Wigner distribution is firstly introduced to extract the time-frequency characteristics from the multi-channel EEG signals. Then multilayer brain network is constructed, with each layer corresponding to a specific frequency band. Such multilayer framework is in line with the nature of the workings of the brain, and can effectively characterize the brain state. Further, a multilayer deep CNN architecture is designed to study the brain network topology features, which is finally used to accurately detect MDD. The experimental results on a publicly available MDD dataset show that the proposed approach is able to detect MDD with state-of-the-art accuracy of 97.27%. Our approach, combining multilayer brain network and deep CNN, enriches the multivariate time series analysis theory and helps to better characterize and recognize the complex brain states.