Assessment of pulse wave velocity through weighted visibility graph metrics from photoplethysmographic signals.

Pulse Wave Velocity (PWV) is a widely recognized non-invasive biomarker of arterial stiffness and an independent predictor of cardiovascular risk, including atherosclerosis, hypertension, and vascular aging. Accurate, accessible estimation of PWV is, therefore, critical for early cardiovascular health detection and monitoring. This study proposes a novel data-driven approach for PWV estimation using features derived from Limited Penetrable Weighted Visibility Graphs (LPWVGs) constructed from photoplethysmography (PPG) waveforms and their first and second derivatives. By generating multiple LPWVGs with diverse weighting strategies, we capture the PPG signal's rich temporal and morphological characteristics. A wide range of features was extracted, including descriptors from two-dimensional Semi-Classical Signal Analysis (SCSA), frequency-domain features, and morphological shape and local variation metrics. These were used to train an Explainable Boosting Machine (EBM), a glass-box machine learning model combining strong predictive power and interpretability. The proposed method was evaluated using positive and negative testing on real multicycle PPG datasets. The results demonstrate high accuracy and robustness, obtaining an [Formula: see text] and [Formula: see text] in the positive test and a [Formula: see text] for the negative test. These results support the feasibility of this approach for non-invasive PWV estimation in clinical and ambulatory settings, with potential applications in cardiovascular disease screening, risk stratification, and aging research.