Key result
Statistical distribution models representing PPG signals undergo testing for motion artifact reduction.
Why the study?
Healthcare automation has heightened the importance of studying PPG signals and their automatic prognosis and diagnosis.
Statistical distribution models can be used to represent PPG signals and reduce motion artifacts.
Statistical PPG modeling may aid artifact reduction in wearables; hypothesis-generating and requires prospective validation before clinical use.
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.
No takes yet. Share an insight, caveat, or question.
Raj et al. (2021) studied Photoplethysmography (PPG) signal analysis. Curve approximation models based on statistical distributions was evaluated on Accuracy of distribution models in representing PPG signals. Mathematical representation of PPG signals using statistical distribution models (Gaussian, Bifurcated Gaussian, Exponentially broadened Gaussian, and lognormal) was tested for motion artifact reduction.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: