Building an Explainable Deep Residual Network for Depression Detection from ECG Signals

This study focuses on developing and validating interpretable deep learning models for depression detection using the proprietary Database. The preprocessing of the proprietary database involves noise reduction, normalization, and segment extraction. The paper proposes a Deep ResNet Network that utilizes overlapping ECG signal segments to capture underlying patterns related to depression. To enhance the understanding of important areas of the input ECG signal in the model, the SHAP (SHapley Additive exPlanations) framework was introduced to increase the interpretability of the results. Independent testing confirmed the robustness and reliability of the Deep ResNet Network, which achieved an accuracy of 92.63%, establishing it as a promising and interpretable tool for future depression diagnosis.