학술논문
RapidHRV: an open-source toolbox for extracting heart rate and heart rate variability.
Document Type
Academic Journal
Author
Kirk PA; Institute of Cognitive Neuroscience, University College London, University of London, London, United Kingdom.; Experimental Psychology, University College London, University of London, London, United Kingdom.; Davidson Bryan A; Independent Scholar, London, United Kingdom.; Garfinkel SN; Institute of Cognitive Neuroscience, University College London, University of London, London, United Kingdom.; Robinson OJ; Institute of Cognitive Neuroscience, University College London, University of London, London, United Kingdom.; Clinical, Educational and Health Psychology, University College London, University of London, London, United Kingdom.
Source
Publisher: PeerJ Inc Country of Publication: United States NLM ID: 101603425 Publication Model: eCollection Cited Medium: Print ISSN: 2167-8359 (Print) Linking ISSN: 21678359 NLM ISO Abbreviation: PeerJ
Subject
Language
English
ISSN
2167-8359
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 (>=10 dB) and moderate-to-excellent recovery of heart rate variability even at relatively low signal to noise ratios (>=20 dB) and sampling rates (>=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.
Competing Interests: Oliver J. Robinson’s MRC senior fellowship is partially in collaboration with Cambridge Cognition (who plan to provide in-kind contribution) and he is running an investigator-initiated trial with medication donated by Lundbeck (escitalopram and placebo, no financial contribution). He also holds an MRC-Proximity to discovery award with Roche (who provide in-kind contributions and have sponsored travel for ACP) regarding work on heart rate variability and anxiety. He has also completed consultancy work on affective bias modification for Peak and online CBT for IESO digital health. Oliver J. Robinson sits on the committee of the British Association of Psychopharmacology.
(© 2022 Kirk et al.)
Competing Interests: Oliver J. Robinson’s MRC senior fellowship is partially in collaboration with Cambridge Cognition (who plan to provide in-kind contribution) and he is running an investigator-initiated trial with medication donated by Lundbeck (escitalopram and placebo, no financial contribution). He also holds an MRC-Proximity to discovery award with Roche (who provide in-kind contributions and have sponsored travel for ACP) regarding work on heart rate variability and anxiety. He has also completed consultancy work on affective bias modification for Peak and online CBT for IESO digital health. Oliver J. Robinson sits on the committee of the British Association of Psychopharmacology.
(© 2022 Kirk et al.)