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Photoplethysmography imaging (PPGI) is an increasingly popular technique for remotely creating signals with a plethora of medical information, referred to as PPGI waveforms. However, PPGI waveforms are often heavily affected by illumination variation and motion artefacts. Current PPGI waveform processing methods are useful for estimating heart rate, however, structural detail is not preserved, rendering the signal incapable of providing additional medical information. For this reason, we propose a multi-scale framework based on the Bayesian residual transform which aims to suppress noise and preserve structural details necessary for extracting cardiovascular information beyond the scope of heart rate. Experiments conducted on a dataset consisting of 24 different PPGI waveforms and corresponding PPG waveforms captured via a finger pulse oximeter suggests a high level of noise and ambient illumination variation suppression is achieved while signal fidelity is largely retained.

A Bayesian Multi-Scale Framework for Photoplethysmogram Imaging Waveform Processing