Photoplethysmographic imaging is an optical solution for non-contact cardiovascular monitoring from a distance. This camera-based technology enables physiological monitoring in situations where contact-based devices may be problematic or infeasible, such as ambulatory, sleep, and multi-individual monitoring. However, automatically extracting the blood pulse waveform signal is challenging due to the unknown mixture of relevant (pulsatile) and irrelevant pixels in the scene. Here, we propose a signal fusion framework, FusionPPG, for extracting a blood pulse waveform signal with strong temporal fidelity from a scene without requiring anatomical priors. The extraction problem is posed as a Bayesian least squares fusion problem, and solved using a novel probabilistic pulsatility model that incorporates both physiologically derived spectral and spatial waveform priors to identify pulsatility characteristics in the scene. Evaluation was performed on a 24-participant sample with various ages (9-60 years) and body compositions (fat% 30.0 ± 7.9, muscle% 40.4 ± 5.3, BMI 25.5 ± 5.2 kg·m-2). Experimental results show stronger matching to the ground-truth blood pulse waveform signal compared to the FaceMeanPPG (p < 0.001) and DistancePPG (p < 0.001) methods. Heart rates predicted using FusionPPG correlated strongly with ground truth measurements (r2 = 0.9952). A cardiac arrhythmia was visually identified in FusionPPG's waveform via temporal analysis.
Keywords: (100.2960) Image analysis; (170.1470) Blood or tissue constituent monitoring; (170.3880) Medical and biological imaging.