A Novel Complex Network-Based Graph Convolutional Network in Major Depressive Disorder Detection

Abstract

As a worldwide disease, major depressive disorder (MDD) severely damages patients’ mental health. It is of great significance of detecting MDD accurately in providing necessary guidance for physicians. Here, a novel complex network-based graph convolutional network (CN-GCN), is developed to detect MDD. First, multichannel electroencephalogram (EEG) signals are decomposed into several frequency bands. Then, a multilayer brain network is constructed via a phase-locking value (PLV), where each layer corresponds to a specific frequency band. Aiming at accurately identifying brain states, the CN-GCN is developed, with multilayer brain network as input. Moreover, power spectral density (PSD) is applied for refining node-level rhythm features. Such structure of CN-GCN allows learning the node features based on the topology connections of the brain network. The proposed framework shows the state-of-the-art (SOTA) detection accuracy of 99.29% on a public MDD dataset. Our work confirms the validity on integrating complex network and GCN in multichannel EEG signal analysis and contributes to identifying complex brain states better.