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

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.

Automated summary

The research developed a method combining complex networks and graph convolutional networks to detect major depressive disorder (MDD) and achieved good detection accuracy on a public dataset.