Diagnostic Support for Major Depressive Disorder: A Multimodal Analysis of EEG and Audio Biomarkers
Depression is a prevalent psychological disorder that affects millions worldwide and poses a diagnostic challenge. Existing criteria, such as the international questionnaires Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and Hamilton Depression Rating Scale (HAMD), while widely used, may not consistently and accurately diagnose depression. In response, recent research efforts have tried to explore promising biomarkers for enhancing depression diagnosis by leveraging electroencephalogram (EEG) data. Using the multimodal MODMA dataset, the proposed framework encompasses EEG and audio data preprocessing, feature extraction, feature selection, and the implementation of a Support Vector Machine (SVM) algorithm. Up to now, EEG and Audio data have been treated separately. For the former, an accuracy of 76% has been achieved while the best result for the later was of 55%. These initial results show the potential of enhancing the classification when using both of them together.