Why the study?
Current methods for cardiovascular disease detection and pulse wave velocity measurement are invasive, expensive, and contact-based, which are not suitable for skin-sensitive patients.
Remote photoplethysmography with a high-speed camera demonstrates feasibility for touchless estimation of local pulse wave velocity and mapping of neck vessels.
May enable non-contact PWV monitoring; leaves open clinical adoption pending validation studies.
Introduction Several population-based clinical studies suggest that increased Pulse Wave Velocity (PWV) is highly associated with increased cardiovascular disease (CVD) mortality, which is one of the leading causes of death worldwide. Current methods for CVD detection are invasive, expensive, and contact methods, which are not friendly for skin-sensitive patients. Methods In this study, we investigated the use of remote photoplethysmography (rPPG) on the neck region using a high-speed camera (2000 frames per second (fps)) to resolve the drawbacks of CVD detection and overcome the limitations of current PWV measurement techniques. Pearson correlation and cross-correlation were used for signal processing and generating the projection map of potential major vessels. A reference signal is selected for the region of interest based on peak value and modulation depth variation. The signal distance and pulse transit time (PPT) between the local and reference signals were calculated using the cross-correlation method and then fitted into a linear regression model for PWV calculation. Results The results revealed areas on the neck that positively and negatively correlated with the selected reference signals, potentially representing the distribution of the main neck vessels - carotid artery and jugular vein- and, consequently, the upstream and downstream blood circulation directions. Discussion This research implies the feasibility of touchless estimation of local PWV using a high-speed camera, expanding the potential applications of remote photoplethysmography in aiding the diagnosis of CVD.
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Cao et al. (2025) studied this question.
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