Abstract
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.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
The authors are grateful to the staff of the CNUH and CNUHH psychiatry research teams for assisting in the collection of patient data, and to Dr. Kim Jae Chang and Dr. Kim Sun Hee for providing the space for data analysis and data collection.
Funding
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.
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MJ: designed the study, contributed to the conceptualization, and drafted and revised the manuscript. JWK: designed the study, contributed to the conceptualization, and drafted and revised the manuscript. KL: contributed to the conceptualization, performed the formal analysis, and drafted the manuscript. DK: contributed to the conceptualization and performed the formal analysis. SHP: curated the data and validated the findings. CHK: curated the data and performed the formal analysis. BTL: curated the data and validated the findings. SYK: curated the data and validated the findings. SWK: curated the data and validated the findings. JMK: contributed to the conceptualization, validated the findings, and reviewed and edited the manuscript. ISS: contributed to the conceptualization, validated the findings, and reviewed and edited the manuscript. YC: designed the study, contributed to the conceptualization, and drafted and revised the manuscript.
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All patients gave written informed consent to participate in the study and use their data. The study was conducted in accordance with the Helsinki Declaration of 1975, as revised in 2008 and approved by the Ethics Commission of the Chonnam National University Hopital Institutional Review Board (CNUH-2021-243, CNUH-2022-216) and the Chonnam National University Hwasun Hospital Institutional Review Board (CNUHH-2021-117, and CNUHH-2022-126) as it uses de-identified data.
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Jhon, M., Kim, JW., Lee, K. et al. Contactless depression screening via facial video-derived heart rate variability. Transl Psychiatry (2026). https://doi.org/10.1038/s41398-026-03831-y
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DOI: https://doi.org/10.1038/s41398-026-03831-y
