Noninvasive Assessment of Arterial Stiffness Using Photoplethysmography: Feature Analysis and Machine Learning-Based Estimation of Carotid-Femoral Pulse Wave Velocity

Cardiovascular diseases are the leading cause of mortality worldwide, underscoring the need for accessible, noninvasive risk assessment. Arterial stiffness, a hallmark of vascular aging, is an established independent predictor of cardiovascular events. While carotid–femoral pulse wave velocity is the clinical gold standard, its use is constrained by specialized equipment and trained personnel. Photoplethysmography, a simple and cost-effective optical technique, offers a promising alternative for wearable technologies. This study analyzes 60 morphological, temporal, and second-derivative features extracted from digital, radial, and brachial arterial signals in a dataset representing 4,374 healthy-adult profiles across six age groups. Correlation and trajectory analyses identified the radial artery as the site with the strongest age association (peak correlation r = 0.75) and revealed site-dependent age trends. Building on these findings, a gradient-boosting regression model trained on radial-artery features estimated carotid–femoral pulse wave velocity using stratified fivefold cross-validation, achieving a mean absolute error of 0.115 ± 0.005 m/s, a root-mean-square error of 0.180 ± 0.011 m/s, and a coefficient of determination of 0.993 ± 0.001. Bland–Altman analysis showed negligible bias (−0.003 m/s) with 95% limits of agreement from −0.357 to 0.351 m/s. Feature-importance analysis corroborated the relevance of interpretable, physiologically meaningful indices. Overall, this work provides a comprehensive cross-site evaluation of photoplethysmography-derived features for arterial-stiffness assessment and demonstrates accurate carotid–femoral pulse wave velocity estimation from the radial artery, supporting integration into wearable systems for continuous vascular-health monitoring.