@article{Chwyl_Amelard_Clausi_Wong_2016, title={A Bayesian Multi-Scale Framework for Photoplethysmogram Imaging Waveform Processing}, volume={2}, url={https://openjournals.uwaterloo.ca/index.php/vsl/article/view/114}, DOI={10.15353/vsnl.v2i1.114}, abstractNote={<p>Photoplethysmography imaging (PPGI) is an increasingly popular<br />technique for remotely creating signals with a plethora of medical<br />information, referred to as PPGI waveforms. However, PPGI waveforms<br />are often heavily affected by illumination variation and motion<br />artefacts. Current PPGI waveform processing methods are useful<br />for estimating heart rate, however, structural detail is not preserved,<br />rendering the signal incapable of providing additional medical information.<br />For this reason, we propose a multi-scale framework based<br />on the Bayesian residual transform which aims to suppress noise<br />and preserve structural details necessary for extracting cardiovascular<br />information beyond the scope of heart rate. Experiments conducted<br />on a dataset consisting of 24 different PPGI waveforms and<br />corresponding PPG waveforms captured via a finger pulse oximeter<br />suggests a high level of noise and ambient illumination variation<br />suppression is achieved while signal fidelity is largely retained.</p>}, number={1}, journal={Journal of Computational Vision and Imaging Systems}, author={Chwyl, Brendan and Amelard, Robert and Clausi, David and Wong, Alexander}, year={2016}, month={Oct.} }