RapidHRV: an open-source toolbox for extracting heart rate and heart rate variability. Kirk, P. A., Davidson Bryan, A., Garfinkel, S. N., & Robinson, O. J. PeerJ, 10:e13147, 2022. Place: United Statesdoi abstract bibtex Heart rate and heart rate variability have enabled insight into a myriad of psychophysiological phenomena. There is now an influx of research attempting using these metrics within both laboratory settings (typically derived through electrocardiography or pulse oximetry) and ecologically-rich contexts (via wearable photoplethysmography, i.e., smartwatches). However, these signals can be prone to artifacts and a low signal to noise ratio, which traditionally are detected and removed through visual inspection. Here, we developed an open-source Python package, RapidHRV, dedicated to the preprocessing, analysis, and visualization of heart rate and heart rate variability. Each of these modules can be executed with one line of code and includes automated cleaning. In simulated data, RapidHRV demonstrated excellent recovery of heart rate across most levels of noise (\textgreater=10 dB) and moderate-to-excellent recovery of heart rate variability even at relatively low signal to noise ratios (\textgreater=20 dB) and sampling rates (\textgreater=20 Hz). Validation in real datasets shows good-to-excellent recovery of heart rate and heart rate variability in electrocardiography and finger photoplethysmography recordings. Validation in wrist photoplethysmography demonstrated RapidHRV estimations were sensitive to heart rate and its variability under low motion conditions, but estimates were less stable under higher movement settings.
@article{kirk_rapidhrv_2022,
title = {{RapidHRV}: an open-source toolbox for extracting heart rate and heart rate variability.},
volume = {10},
copyright = {© 2022 Kirk et al.},
issn = {2167-8359},
doi = {10.7717/peerj.13147},
abstract = {Heart rate and heart rate variability have enabled insight into a myriad of psychophysiological phenomena. There is now an influx of research attempting using these metrics within both laboratory settings (typically derived through electrocardiography or pulse oximetry) and ecologically-rich contexts (via wearable photoplethysmography, i.e., smartwatches). However, these signals can be prone to artifacts and a low signal to noise ratio, which traditionally are detected and removed through visual inspection. Here, we developed an open-source Python package, RapidHRV, dedicated to the preprocessing, analysis, and visualization of heart rate and heart rate variability. Each of these modules can be executed with one line of code and includes automated cleaning. In simulated data, RapidHRV demonstrated excellent recovery of heart rate across most levels of noise ({\textgreater}=10 dB) and moderate-to-excellent recovery of heart rate variability even at relatively low signal to noise ratios ({\textgreater}=20 dB) and sampling rates ({\textgreater}=20 Hz). Validation in real datasets shows good-to-excellent recovery of heart rate and heart rate variability in electrocardiography and finger photoplethysmography recordings. Validation in wrist photoplethysmography demonstrated RapidHRV estimations were sensitive to heart rate and its variability under low motion conditions, but estimates were less stable under higher movement settings.},
language = {eng},
journal = {PeerJ},
author = {Kirk, Peter A. and Davidson Bryan, Alexander and Garfinkel, Sarah N. and Robinson, Oliver J.},
year = {2022},
pmid = {35345583},
pmcid = {PMC8957280},
note = {Place: United States},
keywords = {*Algorithms, *Electrocardiography, Heart Rate/physiology, Heart rate variability, Photoplethysmography, Python, Remote sensing, Toolbox, Wrist},
pages = {e13147},
}
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However, these signals can be prone to artifacts and a low signal to noise ratio, which traditionally are detected and removed through visual inspection. Here, we developed an open-source Python package, RapidHRV, dedicated to the preprocessing, analysis, and visualization of heart rate and heart rate variability. Each of these modules can be executed with one line of code and includes automated cleaning. In simulated data, RapidHRV demonstrated excellent recovery of heart rate across most levels of noise (\\textgreater=10 dB) and moderate-to-excellent recovery of heart rate variability even at relatively low signal to noise ratios (\\textgreater=20 dB) and sampling rates (\\textgreater=20 Hz). Validation in real datasets shows good-to-excellent recovery of heart rate and heart rate variability in electrocardiography and finger photoplethysmography recordings. 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