Curve Approximation Models based on Statistical Distribution with Application to Photoplethysmography (PPG) Signal

Abstract

Photoplethysmography (PPG) signal captures blood volume change in the arteries that carry blood. PPG signals are often used to check the cardiovascular health in patients. Health care automation have made more importance into study of PPG signal and its automatic prognosis and diagnosis. In this paper we aim to achieve motion artifact reduction using low rank minimization and PPG signal representation in mathematical form using statistical distribution models. The proposed approach has been tested for Gaussian, Bifurcated Gaussian, Exponentially broadened Gaussian and lognormal distributions. The accuracy of each distribution in modeling the PPG signal was also studied.