Contactless Depression Screening via Facial Video-derived Heart Rate Variability

Background Depression is a prevalent mental health condition that frequently goes undiagnosed. Heart rate variability (HRV) has emerged as a potential objective marker of depression. Facial video-based HRV measurement offers a novel, contactless approach that could facilitate widespread, non-invasive depression screening.

We analyzed data from 1,453 individuals who completed facial video recordings for HRV analysis 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.

The predictive performance was modest, and HRV alone showed limited discrimination. Additionally, the findings are based on a single cohort and require validation in more diverse populations.

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. Facial video-derived HRV enables non-invasive, contactless depression screening. Stacking ensemble with SVM meta-learner optimized for MCC in depression screening. Combining HRV with demographics improved depression classification vs. HRV alone. Moderate yet consistent performance achieved with minimal, non-invasive inputs. Competing Interest Statement The authors have declared no competing interest. Funding Statement This research was supported by the Bio&Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. RS-2024-00440371) to Jae-Min Kim and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020M3A9G3080281) to Yoonjoo Choi. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRBs of Chonnam National University Hospital and Chonnam National University Hwasun Hospital gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present study are available upon reasonable request to the authors.