Paperdoi:10.1016/j.bspc.2022.104291
Effects of noise and filtering strategies on the extraction of pulse rate variability from photoplethysmograms
Biomedical Signal Processing and Control
TL;DR
Pulse rate variability (PRV) describes the changes in pulse rate through time, when measured using pulsatile signals such as photoplethysmograms (PPG). PRV has been used as a surrogate of heart rate variability (HRV), but their relationship is not straightforward, both due to physiological differences and to effects of technical aspects on the extraction of PRV information from pulsatile signals such as the PPG. One of the factors that may affect PRV analysis is the presence of noise and the filtering strategy used to pre-process the PPG signal.
Abstract
Pulse rate variability (PRV) describes the changes in pulse rate through time, when measured using pulsatile signals such as photoplethysmograms (PPG). PRV has been used as a surrogate of heart rate variability (HRV), but their relationship is not straightforward, both due to physiological differences and to effects of technical aspects on the extraction of PRV information from pulsatile signals such as the PPG. One of the factors that may affect PRV analysis is the presence of noise and the filtering strategy used to pre-process the PPG signal. In this study, the aim was to evaluate the best performing filtering strategy for the extraction of PRV information reliably from noise-contaminated synthetic PPG signals. Time domain, frequency domain and Poincaré plot indices were extracted from PRV trends obtained from the filtered PPG signals and compared against indices measured from a gold-standard simulated PRV function. It was found that PRV information can be reliably extracted from PPG signals filtered using lower low cutoff frequencies and elliptic IIR or equiripple or Parks– McClellan FIR filters, however the filtering parameters depend on the type of noise present in the signal. Moreover, special care should be taken to assess the pNN50 index from contaminated PPG signals, regardless of the type of noise. Future studies should aim to validate these results from real PPG data
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Mejía-Mejía, E., & Kyriacou, P. A. (2023). Effects of noise and filtering strategies on the extraction of pulse rate variability from photoplethysmograms. Biomedical Signal Processing and Control. https://doi.org/10.1016/j.bspc.2022.104291
Mejía-Mejía, Elisa and Panicos A. Kyriacou. 2023. "Effects of noise and filtering strategies on the extraction of pulse rate variability from photoplethysmograms." Biomedical Signal Processing and Control. https://doi.org/10.1016/j.bspc.2022.104291.
Mejía-Mejía, E. and Kyriacou, P. A. (2023) 'Effects of noise and filtering strategies on the extraction of pulse rate variability from photoplethysmograms', Biomedical Signal Processing and Control. doi: 10.1016/j.bspc.2022.104291.
Mejía-Mejía, Elisa, and Panicos A. Kyriacou "Effects of noise and filtering strategies on the extraction of pulse rate variability from photoplethysmograms." Biomedical Signal Processing and Control, 2023. https://doi.org/10.1016/j.bspc.2022.104291.
E. Mejía-Mejía and P. A. Kyriacou, "Effects of noise and filtering strategies on the extraction of pulse rate variability from photoplethysmograms," Biomedical Signal Processing and Control, 2023, doi: 10.1016/j.bspc.2022.104291.
@article{mejiamejia2023effects,
title = {{Effects of noise and filtering strategies on the extraction of pulse rate variability from photoplethysmograms}},
author = {Mejía-Mejía, Elisa and Kyriacou, Panicos A.},
year = {2023},
journal = {Biomedical Signal Processing and Control},
doi = {10.1016/j.bspc.2022.104291}
}Added to the ChatPPG research hub
