  Photolethysmographic imaging (PPGI) is a widefield non-contact biophotonic
technology able to remotely monitor cardiovascular function over anatomical
areas. Though spatial context can provide increased physiological insight,
existing PPGI systems rely on coarse spatial averaging with no anatomical
priors for assessing arterial pulsatility. Here, we developed a continuous
probabilistic pulsatility model for importance-weighted blood pulse waveform
extraction. Using a data-driven approach, the model was constructed using a 23
participant sample with large demographic variation (11/12 female/male, age
11-60 years, BMI 16.4-35.1 kg$\cdot$m$^{-2}$). Using time-synchronized
ground-truth waveforms, spatial correlation priors were computed and projected
into a co-aligned importance-weighted Cartesian space. A modified
Parzen-Rosenblatt kernel density estimation method was used to compute the
continuous resolution-agnostic probabilistic pulsatility model. The model
identified locations that consistently exhibited pulsatility across the sample.
Blood pulse waveform signals extracted with the model exhibited significantly
stronger temporal correlation ($W=35,p<0.01$) and spectral SNR ($W=31,p<0.01$)
compared to uniform spatial averaging. Heart rate estimation was in strong
agreement with true heart rate ($r^2=0.9619$, error $(\mu,\sigma)=(0.52,1.69)$
bpm).
