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Contactless depression screening via facial video-derived heart rate variability.

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Objective depression screening using facial video-based heart rate variability (HRV) shows promise. Combining HRV with demographics moderately identified depressive symptoms, suggesting a potential non-invasive tool for scalable mental health assessment.

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Area of Science:

  • Psychiatry and Mental Health
  • Biomedical Engineering
  • Computational Health Science

Background:

  • Depression is a common, often undiagnosed mental health condition.
  • Objective and scalable screening tools are needed for early detection.
  • Heart rate variability (HRV) is a potential physiological indicator of depression.

Purpose of the Study:

  • To investigate the efficacy of facial video-derived HRV for detecting depressive symptoms.
  • To develop and evaluate a machine learning model combining HRV and demographic data for depression screening.
  • To assess the feasibility of a contactless, non-invasive approach for large-scale mental health assessment.

Main Methods:

  • Analysis of facial video recordings and Patient Health Questionnaire-9 (PHQ-9) scores from 1453 individuals.
  • Development of a stacking ensemble classifier integrating HRV features and demographic information (smoking status, sex, comorbidities).
  • Evaluation of model performance using 5-fold cross-validation, reporting AUROC, AUPRC, and MCC.

Main Results:

  • The stacking model achieved an AUROC of 0.64, indicating moderate discrimination ability.
  • Incorporating demographic features alongside HRV improved classification performance compared to HRV alone.
  • Smoking status, sex, and medical comorbidities were identified as significant predictors.

Conclusions:

  • Facial video-derived HRV, augmented by demographic factors, offers a moderately effective contactless method for identifying individuals with depressive symptoms.
  • While performance is modest, this non-invasive approach holds potential for accessible, large-scale depression screening.
  • Further research may refine this method for broader clinical application in mental health assessment.