Machine Learning Framework for Classifying and Predicting Depressive Behavior Based on PPG and ECG Feature Extraction

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Depression is a significant risk factor for other serious health conditions, such as heart failure, dementia, and diabetes. In this study, a quantitative method was developed to detect depressive states in individuals using electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Data were obtained from 59 people affiliated with the high-specialized medical center of Bajio T1, which consists of medical professionals, administrative personnel, and service workers. Data were analyzed using the Beck Depression Inventory (BDI-II) to discern potential false positives. The statistical analyses performed elucidated distinctive features with variable behavior in response to diverse physical stimuli, which were adeptly processed through a machine learning classification framework. The method achieved an accuracy rate of up to 92% in the identification of depressive states, substantiating the potential of biophysical data in increasing the diagnostic process of depression. The results suggest that this method is innovative and has significant potential. With additional refinements, this approach could be utilized as a screening tool in psychiatry, incorporated into everyday devices for preventive diagnostics, and potentially lead to alarm systems for individuals with suicidal thoughts.