Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems
This work addresses the need for more accurate and spatially informed cardiovascular monitoring in PPGI systems, representing an incremental improvement over existing methods.
The researchers tackled the problem of coarse spatial averaging in photoplethysmographic imaging (PPGI) systems by developing a continuous probabilistic pulsatility model for importance-weighted blood pulse waveform extraction, resulting in significantly stronger temporal correlation and spectral SNR compared to uniform averaging, with heart rate estimation showing strong agreement (r²=0.9619, error (μ,σ)=(0.52,1.69) bpm).
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 $(μ,σ)=(0.52,1.69)$ bpm).