Prescreening depression using wearable electrocardiogram and photoplethysmogram data from a psycholinguistic experiment.

Abstract

Objective: Depression is a prevalent mental health disorder that significantly impacts well-being and quality of life. This study investigates the relationship between depression and cardiovascular function, exploring time-series features derived from electrocardiogram (ECG) and photoplethysmogram (PPG) data as potential biomarkers for depression prescreening. Approach: As part of a comprehensive psycholinguistic experiment, we collected data from 60 individuals, including both healthy participants and those with varying levels of depression, assessed using the Beck Depression Inventory-II (BDI-II) and the Patient Health Questionnaire-9 (PHQ-9). Bimodal features derived from both ECG and PPG data were used to develop machine learning models for depression risk classification, employing classifiers such as Random Forest, XGBoost, Logistic Regression, and Support Vector Machines (SVM). Additionally, regression models were built to predict depression severity based on ECG- and PPG-derived biomarkers. Main Results: Key findings indicate that short-term variability (SD1) features in the ECG RR interval, peripheral systolic and diastolic phases from the PPG, and pulse duration significantly differ between healthy individuals and those at risk of depression. SVM achieved the best classification performance, with an AUROC of 0.83 ± 0.11 for BDI-II-based classification and 0.78 ± 0.11 for PHQ-9-based classification. SHAP analysis consistently identified systolic-SD1 and RR-SD1 as key predictors. Regression analysis further supported the role of cardiovascular features in assessing depression severity, yielding a mean absolute error (MAE) of 10.18 for BDI-II and 5.27 for PHQ-9 score regression. Significance: This study demonstrates the feasibility of using wearable ECG and PPG technologies for depression prescreening. The findings suggest that cardiac activity-based biomarkers can contribute to the development of cost-effective, objective, and non-invasive tools for mental health assessment, complementing traditional diagnostic methods.