Early adolescent depression detection system based on the transformer model

Depression is one of the more prevalent mental health disorders, characterized primarily by low mood. Recently, there has been a striking shift, and increasingly younger demographics have started showing symptoms of the same, particularly in adolescents. This paper proposes a new method for the automatic detection of incipient adolescent depression by use of deep multimodal learning techniques. This aims at improving preparedness to better face the rising problem of adolescent depression. In the proposed approach, unimodal features are extracted from electroencephalography (EEG), electrocardiogram (ECG), and speech signals using the Transformer model and subsequently fused into a comprehensive multimodal feature set for binary classification. The model does not only increase its generalizability by fusing different physiological signals but also increases the accuracy and reliability of diagnostic results by fusing multimodal features.