A Novel Spatial-Temporal Graph Neural Network for Major Depressive Disorder Detection Based on Resting State EEG Signals

Major Depressive Disorder (MDD) presents a substantial global health challenge due to its high prevalence of morbidity, disability, and mortality, along with frequent underdiagnosis. The profound impact of MDD on patients' mental health emphasizes the necessity for precise and objective diagnostic tools. However, existing studies have mostly overlooked the topological relationships among brain channels and the spatio-temporal correlations in brain activity. To discover the latent patterns within the complex neural networks associated with depression, we present a novel model, the Spatio-Temporal Graph Convolutional Network-Convolutional Neural Network-Attention Mechanism (ST-GCA), based on deep learning and complex network theories, aiming to enhance MDD detection. The proposed model utilized brain network connectivity matrices to quantify the connection lengths between channels, followed by a convolutional neural network to extract deep spatial features. Moreover, a spatio-temporal attention mechanism was integrated to adaptively capture critical sequential information from segmented electroencephalogram (EEG) signals of patients with MDD. This approach facilitated the analysis of distinct neural mechanisms in MDD patients compared to healthy controls (HCs). The ST-GCA framework achieved an accuracy of 97.68% in subject-dependent experiments and 93.93% in subject-independent experiments on a publicly available dataset, demonstrating its effectiveness in analyzing MDD-related EEG signals. These findings provide a promising tool to assist clinicians in diagnosing MDD, thereby offering valuable clinical guidance.