Does a stacking ensemble classifier using facial video-derived heart rate variability and demographic information accurately classify individuals with depressive symptoms?
Facial video-derived HRV combined with demographic factors provides a modest but promising contactless approach for large-scale depression screening.
Depression is a prevalent mental health condition that frequently remains undiagnosed, highlighting the need for objective and scalable screening tools. Heart rate variability (HRV) has emerged as a potential physiological marker of depression, and facial video-based HRV measurement offers a novel, contactless approach that could facilitate widespread, non-invasive depression screening. We analyzed data from 1453 individuals who completed facial video recordings and the Patient Health Questionnaire-9 (PHQ-9). A stacking ensemble classifier was developed using HRV features and basic demographic information to classify individuals with depressive symptoms. The ensemble incorporated four base learners (logistic regression, gradient boosting, XGBoost, and SVM) with an SVM meta-learner. Model performance was evaluated using 5-fold cross-validation. The stacking model achieved its best discrimination of AUROC 0.64 (AUPRC 0.45 and MCC 0.21). Incorporating demographic features alongside HRV improved performance over HRV alone. Feature importance analysis revealed that smoking status, sex, and medical comorbidities were the strongest contributors to the predictions. Facial video-derived HRV, combined with simple demographic factors, can moderately distinguish individuals with depressive symptoms in a contactless manner. Although predictive performance was modest, this non-invasive approach shows promise for accessible, large-scale depression screening.
Jhon et al. (Wed,) studied this question.