Depression Severity Identification Based on Shallow 2d Self-Attention-Cnn Using Eeg Functional Connectivity Network

Background and objectives: Depression inflicts significant harm on both society and family. Previous studies have indicated that the functional network of EEG signals worked well in recognizing major depression. This study aims to further identify depression severities and characterize their EEG functional network difference by designing a deep learning strategy and a corresponding visualization method.Methods: Rest state EEG recordings from 30 healthy controls, 35 mild depressed and 26 severe depressed patients were included. Weighted phase lag indexes were computed across four frequency sub-bands to delineate the functional connectivity of EEG networks, serving as the input matrix. To better adapt volume conduction effects, a shallow CNN-based incorporated with 2D Self-Attention architecture was designed, enabling the model to capture information across diverse spans and scales within the functional connectivity (FC) matrix. Leveraging the Grad-CAM algorithm, the model highlighted crucial FCs and corresponding EEG pairs for classification. Finally, the changes of EEG FC network across depression severities were statistically analyzed and manifested.Results: An accuracy of 89.2% was achieved in tri-classification of 10-second EEG segments using 52 EEG channels, remaining high at 84.1% with 30 selected channels. Notably, the investigation revealed that significant FC changes from mild to severe depression did not exhibit a simple or monotonous pattern.Conclusions: This research presented a directly measured methodology to identify depression severity, vital for informing prevention and therapeutic interventions. Furthermore, the findings shed light on the evolving patterns of brain function as depression progresses. The proposed deep learning model and channel selection algorithm offer potential applications beyond this study, promising broader utility in EEG-based research endeavors.