Diagnosing Depression Using Different Approaches

DIAGNOSING DEPRESSION USING DIFFERENT APPROACHES Introduction  Depression a major health concern, affects mental state and communication  Questionnaires and scales used for clinical diagnosis  Machine learning techniques explored recently for automatic diagnosis Challenges  No specific symptoms, difficult to detect in early stages  No standard laboratory tests, subjective clinical assessments  Multifactor disorder, needs information from multiple sources EEG-Based Diagnosis  Non-invasive, cost effective, correlates brain activity with depression  Techniques use EEG features like entropy, fractal dimensions etc.  Classifiers: SVM, Random Forest, Deep Learning models  Accuracies up to 99% reported on EEG datasets Multimodal Diagnosis  Combines multiple modalities like EEG, MRI, speech, video, texts  Provides more information than single modality approaches  Fusion of features and classifier outputs  Improves accuracy and robustness compared to single modality Conclusion  Automatic diagnosis can aid clinicians and enable early detection  EEG provides good discrimination capability  Multimodal fusion recommended for handling complexity of depression Key Highlights  Machine learning has potential for automatic depression screening  EEG a promising modality, provides brain activity patterns  Multimodal fusion improves performance compared to single modality  Future work needed in multi-modal fusion and interpretable models