A fixed-interval Kalman smoother reduced motion artifacts from photoplethysmography signals more effectively than conventional adaptive filtering algorithms.
A photoplethysmography (PPG) signal provides very useful information about a subject's hemodynamic status in a hospital or ubiquitous environment. However, PPG is very vulnerable to motion artifacts, which can significantly distort the information belonging to the PPG signal itself. Thus, the reduction of the effects of motion artifacts is an important issue when monitoring the cardiovascular system by PPG. There have been many adaptive techniques to reduce motion artifacts from PPG signals. In the present study, we compared a method based on the fixed-interval Kalman smoother with the usual adaptive filtering algorithms, e.g. the normalized least mean squares, recursive least squares and the conventional Kalman filter. We found that the fixed-interval Kalman smoother reduced motion artifacts from the PPG signal most effectively. Therefore, the use of the fixed-interval Kalman smoother can reduce motion artifacts in PPG, thus providing the most reliable information that can be deduced from the reconstructed PPG signals.
Lee et al. (Wed,) conducted a other in Motion artifacts in photoplethysmography (PPG) signals. Fixed-interval Kalman smoother vs. Normalized least mean squares, recursive least squares, and conventional Kalman filter was evaluated on Reduction of motion artifacts from the PPG signal. A fixed-interval Kalman smoother reduced motion artifacts from photoplethysmography signals more effectively than conventional adaptive filtering algorithms.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: