Major Depressive Disorder Detection Using Effective Connectivity of EEG Signals and Deep Learning Transformer Model

A serious public health problem, major depression disorder is characterised by a person's increased risk of suicidal thoughts, chronic melancholy, and disinterest in once-enjoyable activities. Inaccurate diagnosis and inadequate treatment are frequently the results of surveys and psychiatric examinations of MDD. This work aims to assess the use of effective connectivity (EC) and vision transformers (ViT) to address complexities associated with interpreting the intricate temporal and spatial properties of EEG data. This study estimated the EC within the brain default mode network (DMN) using the EEG signals from 30 MDD and 30 healthy controls (HC). The six key regions of the DMN's effective connectivity are taken from a 3D matrix and placed into a 2D matrix for a suitable input into a deep learning model. Next, a vision transformer (ViT) model is trained, validated, and tested using the 2D picture of EC. The results show that the proposed MDD diagnosis algorithm achieved 82.17% accuracy with an AUC of 0.887 in classifying MDD and HC test subjects. The model's accuracy decreased with the introduction of new subjects due to increasing variation in the EC images of MDD and HC classes.