Longitudinal Tracking of Valve Regurgitation from Peripheral Photoplethysmography Using a 1D Hemodynamic Surrogate: a Joint MCMC-Kalman Framework

AO_SCPLOWBSTRACTC_SCPLOWAortic (AR) and mitral (MR) regurgitation are progressive valvulopathies whose management depends on tracking severity over months to years, currently via infrequent clinic-bound echocardiography. We investigated whether peripheral photoplethysmography (PPG) can track AR and MR longitudinally despite unknown day-to-day hemodynamic variation. A one-dimensional Navier-Stokes arterial model from aortic root to pedal digital artery, with parametric AR and MR at the inlet, generated distal waveforms from which we extracted eleven morphological features. Univariate sensitivity, Cramer- Rao bound, and feature-ablation analyses quantified theoretical observability under realistic noise and physiological variability. A neural surrogate of the 1D model was embedded in a joint Markov Chain Monte Carlo (MCMC)-Kalman scheme that tracked regurgitant fraction monthly while marginalizing over unknown systemic parameters. Across 240 synthetic patients spanning stable and high-risk AR and MR, with and without progressive hypertension, over 36 months, the tracker achieved RMSE of 0.023-0.028 for AR and 0.075-0.101 for MR in regurgitant-fraction units. Dicrotic notch timing was most informative for both valves; augmentation index was uniquely critical for MR. Progressive hypertension paradoxically improved MR tracking by 16-18%. MCMC-Kalman tracking errors arose mainly from over-constrained latent parameters. These results support the theoretical feasibility of wearable PPG-based surveillance of valve regurgitation and motivate prospective clinical validation.