Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems

TL;DR

This paper introduces a probabilistic spatial model for photoplethysmographic imaging that improves pulse waveform extraction by leveraging anatomical priors, resulting in more accurate cardiovascular monitoring.

Contribution

A novel data-driven probabilistic pulsatility model for PPGI that enhances waveform extraction by incorporating spatial and anatomical information.

Findings

Improved temporal correlation and spectral SNR in extracted waveforms.

Heart rate estimation closely matches ground-truth measurements.

Model identifies consistent pulsatility locations across diverse subjects.

Abstract

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).