Remote photoplethysmography from facial videos successfully extracted heart rate variability features to recognize depression levels, offering performance comparable to deep learning-based approaches.
Can remote photoplethysmography from facial videos accurately recognize different levels of depression based on HRV features?
Remote photoplethysmography from facial videos can extract HRV features to recognize depression levels without contact-based sensors, performing comparably to deep learning and other audiovisual methods.
Depression is a mental illness that may be harmful to an individual's health. The detection of mental health disorders in the early stages and a precise diagnosis are critical to avoid social, physiological, or psychological side effects. This work analyzes physiological signals to observe if different depressive states have a noticeable impact on the blood volume pulse (BVP) and the heart rate variability (HRV) response. Although typically, HRV features are calculated from biosignals obtained with contact-based sensors such as wearables, we propose instead a novel scheme that directly extracts them from facial videos, just based on visual information, removing the need for any contact-based device. Our solution is based on a pipeline that is able to extract complete remote photoplethysmography signals (rPPG) in a fully unsupervised manner. We use these rPPG signals to calculate over 60 statistical, geometrical, and physiological features that are further used to train several machine learning regressors to recognize different levels of depression. Experiments on two benchmark datasets indicate that this approach offers comparable results to other audiovisual modalities based on voice or facial expression, potentially complementing them. In addition, the results achieved for the proposed method show promising and solid performance that outperforms hand-engineered methods and is comparable to deep learning-based approaches.
Casado et al. (Fri,) conducted a other in Depression. Remote photoplethysmography (rPPG) from facial videos vs. Other audiovisual modalities, hand-engineered methods, and deep learning-based approaches was evaluated on Recognition of different levels of depression. Remote photoplethysmography from facial videos successfully extracted heart rate variability features to recognize depression levels, offering performance comparable to deep learning-based approaches.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: