The DOI matches its Crossref record; 112 of the 114 references it cites were found in a registry or an index.
How accurate is pulse rate variability as an estimate of heart rate variability?
Abstract
Background The usefulness of heart rate variability (HRV) as a clinical research and diagnostic tool has been verified in numerous studies. The gold standard technique comprises analyzing time series of RR intervals from an electrocardiographic signal. However, some authors have used pulse cycle intervals instead of RR intervals, as they can be determined from a pulse wave (e.g. a photoplethysmographic) signal. This option is often called pulse rate variability (PRV), and utilizing it could expand the serviceability of pulse oximeters or simplify ambulatory monitoring of HRV.Methods We review studies investigating the accuracy of PRV as an estimate of HRV, regardless of the underlying technology (photoplethysmography, continuous blood pressure monitoring or Finapresi, impedance plethysmography).Results/conclusions Results speak in favor of sufficient accuracy when subjects are at rest, although many studies suggest that short-term variability is somewhat overestimated by PRV, which reflects coupling effects between respiration and the cardiovascular system. Physical activity and some mental stressors seem to impair the agreement of PRV and HRV, often to an inacceptable extent. Findings regarding the position of the sensor or the detection algorithm are not conclusive. Generally, quantitative conclusions are impeded by the fact that results of different studies are mostly incommensurable due to diverse experimental settings and/or methods of analysis.
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“A comparative review of photoplethysmography against ECG stated that photoplethysmography can be used during rest but not during stress (Schafer and Vagedes, 2013).”
From Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research – Recommendations for Experiment Planning, Data Analysis, and Data Reporting (2017) · cites this paper as Schafer and Vagedes, 2013
In the citing paper
“However, it cannot yet be considered to be a surrogate during exercise or stress [17], [129].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [17]
In the citing paper
“Nonetheless, reviews have concluded that PRV is a good surrogate of HRV for certain applications at rest.”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [17]
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“However, pulse derived HRV have been compared to HRV derived from ECG in several studies and have demonstrated sufficient accuracy in subjects at rest [29–31], although short term variability such as the HF component may be overestimated with standing and exercise due to variations in pulse transit time [31].”
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“The technique used to define and detect the limits between pulse cycles influences the resulting time series of pulse-to-pulse intervals (Schäfer and Vagedes, 2013).”
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“Its usefulness as a clinical research and diagnostic tool has been verified in numerous studies [47] and adopted by the NrPPG researchers as a pulse rate variability (PRV) [33], [34], [38], [44], [48], [50], i.”
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“That is, using different measurement locations would have implied less fairly comparable measurements, also due to the higher eccrine sweat glands density in the hands compared to other body sites [100] and the different pulse transit time implied by hand and head PPG sites [91].”
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“Especially note we can take Pulse to Pulse Interval (PPI) of the patient from the socalled heart rate watch by Photoplethysmography (PPG) as similar RRI even when the real RRI information (HRV related) is not monitored [29][30][31].”
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“Although the PI variability assessment may be considered may be less accurate than measuring heart rate variability by electrocardiogram, some studies have demonstrated the agreement between heart rate variability and PI variability (31, 32).”
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“Note that, although we refer to HR and HRV, studies also use pulse rate (variability) to refer to data that is obtained with a PPG sensor as opposed to heart rate variability to refer to data that is obtained with an electrocardiogram (Schäfer and Vagedes, 2013).”
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“This conclusion is supported by previous studies performed by using different approaches working in discrete time on interval time series, which suggests that the differences between heart rate and pulse rate variability are due not only to measurement noise, but also to physiological factors (Schäfer and Vagedes, 201…”
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“As a result, to evaluate HRV, the standard deviation of NN intervals (SDNN) is usually used [50], [51], [52].”
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“However, they were measured from healthy, young, and resting subjects, which might diminish the differences between HRV and PRV [8], [9].”
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“The non-invasive, cost-effective, and straightforward characteristics of HRV index measurements are one of the reasons of being used for clinical research and very suitable as a medium for observation [35].”
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On the spatial phase distribution of cutaneous low-frequency perfusion oscillations
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State of the science: heart rate variability in health and disease
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“Another limitation is that HRV indices were measured via PPG instead of electrocardiogram (ECG), the gold standard for HRV index acquisition [77].”
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Digital Health and Care Study on Elderly Monitoring
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The Physiological Response during Divergent Thinking
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“Heart rate variability (HRV) is the variation in the time interval between heartbeats (also known as the inter-beat intervals), and can be measured from the variation in consecutive R-R intervals of the Electrocardiogram (ECG) or be determined from consecutive pulse cycle intervals measured using techniques such as ph…”
MethodA Hybrid Wavelet-Based Method for the Peak Detection of Photoplethysmography Signals
Li, S., Jiang, S., Jiang, S., Wu, J., Xiong, W., & Diao, S. (2017) · Computational and Mathematical Methods in Medicine · doi:10.1155/2017/9468503 · Cited by 15
“Previous studies have shown that the pulse rate and pulse-to-pulse interval from PPG are highly correlated with the heart rate and R-R interval from ECG, which indicates that heart rate variability (HRV) analysis can be alternated by pulse rate variability (PRV) analysis [3, 4].”
BackgroundNoncontact measurement of emotional and physiological changes in heart rate from a webcam
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Comparison of Heart Rate Variability Responses to Varying Resistance Exercise Volume-Loads
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“Also, respiration-induced changes in intrathoracic pressure caused by the Valsalva maneuver utilized during resistance exercise are known to cause increases in blood pressure (Schafer & Vagedes, 2013).”
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“These findings show sufficient accuracy under non-stationary conditions, but results regarding the position of the sensor are not conclusive (Schäfer and Vagedes, 2013).”
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“Although PPG-and ECG-derived HRV metrics have generally good agreement, PPG-based measures can differ from ECG-based measures in the presence of physical activity, cold exposure, and other factors [6-8].”
BackgroundA novel wearable sensor device for continuous monitoring of cardiac activity during sleep
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“A detailed description of the different HRV parameters can be found in [17]”
BackgroundComparison of multiple cardiac signal acquisition technologies for heart rate variability analysis
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“HRV, Attention, Stress and Flow Heart rate variability (HRV) can be measured from the variation in consecutive R-R intervals of the Electrocardiogram (ECG) or from the variation in consecutive pulse cycle intervals using techniques such as Photoplethysmography [13][14][15].”
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“The correspondence of PRV with heart rate variability in the resting state is well documented [13, 26].”
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Ectopic Beat Detection from Wrist Optical Signals for Sinus Rhythm and Atrial Fibrillation Subjects
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Sleep and Circadian Regulation of the Autonomic Nervous System
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CameraHRV: Robust measurement of heart rate variability using a camera
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“PRV is equivalent to HRV obtained from the ECG signal when the subject is at rest.(2) HRV parameters are potential indicators for stress levels and cardiac health.”
BackgroundPhotoplethysmographic Waveform and Pulse Rate Variability Analysis in Hyperbaric Environments
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Platform for Analysis and Labeling of Medical Time Series
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“Measuring arterial responses at the fingertips may be potentially influenced by these physiological factors during PPG assessment (12, 19).”
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Metadata
- Authors
- Schäfer, A., & Vagedes, J.
- Affiliations
- Academic Research in Complementary and Integrative Medicine (DE)
- University Children's Hospital Tübingen (DE)
- Corresponding author
- Vagedes, Jan
- ORCID
- Vagedes, Jan
- Venue
- International Journal of Cardiology
- Publisher
- Elsevier BV
- Published
- 2013-06
- Volume
- 166
- Issue
- 1
- Pages
- 15-29
- ISSN
- 0167-5273 (ISSN-L)
- OpenAlex
- W188715143
- Cited by
- 661
- References
- 114
- Citations by year
- 2026: 59 · 2025: 51 · 2024: 56 · 2023: 52 · 2022: 57 · 2021: 72 · 2020: 67 · 2019: 49 · 2018: 59 · 2017: 40 · 2016: 40 · 2015: 29 · 2014: 19 · 2013: 9
- FWCI
- 11.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Disease Prevention
- Keywords
- pulse rate variability, heart rate variability, photoplethysmography, pulse oximetry, RR interval, measurement accuracy, physical activity, psychological stress, sensor position, detection algorithm, ambulatory monitoring
- MeSH terms
- Electrocardiography, Heart Rate, Humans, Motor Activity, Pulse, Rest, Photoplethysmography
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