Paperdoi:10.1088/1361-6579/ac826d
Detecting beats in the photoplethysmogram: benchmarking open-source algorithms
Physiological Measurement
(opens in a new tab)Peter H Charlton et al., "Detecting beats in the photoplethysmogram: benchmarking open-source algorithms", doi:10.1088/1361-6579/ac826d, CC BY 4.0
TL;DR
The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches. A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat detection algorithms have been proposed, although it is not clear which performs best.
Abstract
The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches. A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat detection algorithms have been proposed, although it is not clear which performs best. Objective: This study aimed to: (i) develop a framework with which to design and test PPG beat detectors; (ii) assess the performance of PPG beat detectors in different use cases; and (iii) investigate how their performance is affected by patient demographics and physiology. Approach: Fifteen beat detectors were assessed against electrocardiogram-derived heartbeats using data from eight datasets. Performance was assessed using the F 1 score, which combines sensitivity and positive predictive value. Main results: Eight beat detectors performed well in the absence of movement with F 1 scores of ≥90% on hospital data and wearable data collected at rest. Their performance was poorer during exercise with F 1 scores of 55%–91%; poorer in neonates than adults with F 1 scores of 84%–96% in neonates compared to 98%–99% in adults; and poorer in atrial fibrillation (AF) with F 1 scores of 92%–97% in AF compared to 99%–100% in normal sinus rhythm. Significance: Two PPG beat detectors denoted ‘MSPTD’ and ‘qppg’ performed best, with complementary performance characteristics. This evidence can be used to inform the choice of PPG beat detector algorithm. The algorithms, datasets, and assessment framework are freely available.
Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: http://creativecommons.org/licenses/by/4.0/
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Cite this paper
Charlton, P. H., Kotzen, K., Mejía-Mejía, E., Aston, P. J., Budidha, K., Mant, J., Pettit, C., Behar, J. A., & Kyriacou, P. A. (2022). Detecting beats in the photoplethysmogram: benchmarking open-source algorithms. Physiological Measurement. https://doi.org/10.1088/1361-6579/ac826d
Charlton, Peter H, Kevin Kotzen, Elisa Mejía-Mejía, Philip J Aston, Karthik Budidha, Jonathan Mant, Callum Pettit, Joachim A Behar, and Panicos A Kyriacou. 2022. "Detecting beats in the photoplethysmogram: benchmarking open-source algorithms." Physiological Measurement. https://doi.org/10.1088/1361-6579/ac826d.
Charlton, P. H., Kotzen, K., Mejía-Mejía, E., Aston, P. J., Budidha, K., Mant, J., Pettit, C., Behar, J. A. and Kyriacou, P. A. (2022) 'Detecting beats in the photoplethysmogram: benchmarking open-source algorithms', Physiological Measurement. doi: 10.1088/1361-6579/ac826d.
Charlton, Peter H, et al. "Detecting beats in the photoplethysmogram: benchmarking open-source algorithms." Physiological Measurement, 2022. https://doi.org/10.1088/1361-6579/ac826d.
P. H. Charlton et al., "Detecting beats in the photoplethysmogram: benchmarking open-source algorithms," Physiological Measurement, 2022, doi: 10.1088/1361-6579/ac826d.
@article{charlton2022detecting,
title = {{Detecting beats in the photoplethysmogram: benchmarking open-source algorithms}},
author = {Charlton, Peter H. and Kotzen, Kevin and Mejía-Mejía, Elisa and Aston, Philip J. and Budidha, Karthik and Mant, Jonathan and Pettit, Callum and Behar, Joachim A. and Kyriacou, Panicos A.},
year = {2022},
journal = {Physiological Measurement},
doi = {10.1088/1361-6579/ac826d}
}Added to the ChatPPG research hub
