Cited by 661Heart Rate Variability and Autonomic ControlNon-Invasive Vital Sign MonitoringCardiovascular Health and Disease Prevention

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How accurate is pulse rate variability as an estimate of heart rate variability?

Schäfer, Axel, Vagedes, Jan·2013·International Journal of Cardiology

DOI 10.1016/j.ijcard.2012.03.119

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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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BackgroundMethod
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Background
Heart Rate Variability and Its Relation to Chronic Kidney Disease: Results From the PREVEND Study
Thio, C. H. L., van Roon, A. M., Lefrandt, J. D., Gansevoort, R. T., & Snieder, H. (2018) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000556 · Cited by 35
“In individuals at rest, pulse rate variability is considered an accurate estimate of HRV (43).”
Background
Assessing Drivers’ Physiological Responses Using Consumer Grade Devices
Gruden, T., Stojmenova, K., Sodnik, J., & Jakus, G. (2019) · Applied Sciences · doi:10.3390/app9245353 · Cited by 34
Association of Heart rate variability measured by RR interval from ECG and pulse to pulse interval from Photoplethysmography
Kumar, C. K., Manaswini, M., Maruthy, K. N., Kumar, A. V. S., & kumar, K. M. (2021) · Clinical Epidemiology and Global Health · doi:10.1016/j.cegh.2021.100698 · Cited by 34
Video pulse rate variability analysis in stationary and motion conditions
Rodríguez, A., & Ramos-Castro, J. J. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0437-0 · Cited by 35
“Moreover, some authors have investigated the possibility of measuring pulse rate variability (PRV) as a surrogate of HRV showing close agreement [10, 11].”
Background
Measuring Heart Rate Variability in Free-Living Conditions Using Consumer-Grade Photoplethysmography: Validation Study
Lam, E., Aratia, S., Wang, J., & Tung, J. Y. (2020) · JMIR Biomedical Engineering · doi:10.2196/17355 · Cited by 36
A guide to consumer-grade wearables in cardiovascular clinical care and population health for non-experts
Jamieson, A., Chico, T., Jones, S. H., Chaturvedi, N., Hughes, A. D., & Orini, M. (2025) · npj Cardiovascular Health · doi:10.1038/s44325-025-00082-6 · Cited by 41
“When measured using PPG, the most appropriate metric to use would be pulse rate variability 44,45 , which is derivedfromconsecutivepulsesrecordedinthewristor ﬁ ngerandnotfrom consecutive R-R intervals on an ECG.”
Influential
Quantitative Comparison of the Performance of Piezoresistive, Piezoelectric, Acceleration, and Optical Pulse Wave Sensors
Wang, H., Wang, L., Sun, N., Yao, Y., Hao, L., Xu, L., & Greenwald, S. E. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2019.01563 · Cited by 39
“Many types of sensors have been developed for the non-invasive detection of pulse waves (Schafer and Vagedes, 2013; Meidert et al., 2014; Stea et al., 2014; Boutry et al., 2015; Kamshilin et al., 2016).”
Background
Neural correlates of fluctuations in the intermediate band for heart rate and respiration are related to interoceptive perception
Keller, M., Pelz, H., Perlitz, V., Zweerings, J., Roecher, E., Baqapuri, H. I., & Mathiak, K. (2020) · Psychophysiology · doi:10.1111/psyp.13594 · Cited by 33
Comparison of Pulse Wave Signal Monitoring Techniques with Different Fiber-Optic Interferometric Sensing Elements
Ushakov, N. A., Александрович, М. А., Kulik, D., & Борисович, Л. Л. (2021) · Photonics · doi:10.3390/photonics8050142 · Cited by 33
Heart Rate Variability and Its Ability to Detect Worsening Suicidality in Adolescents: A Pilot Trial of Wearable Technology
Sheridan, D. C., Baker, S. D., Dehart, R., Lin, A. L., Hansen, M. L., Tereshchenko, L. G., Le, N., Newgard, C. D., & Nagel, B. J. (2021) · Psychiatry Investigation · doi:10.30773/pi.2021.0057 · Cited by 33
Reduced heart rate variability is related to the number of metabolic syndrome components and manifest diabetes in the sixth Tromsø study 2007–2008
Azulay, N., Olsen, R. B., Nielsen, C. S., Stubhaug, A., Jenssen, T. G., Schirmer, H., Frigessi, A., Rosseland, L. A., & Tronstad, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-15824-0 · Cited by 33
Sinus or not: a new beat detection algorithm based on a pulse morphology quality index to extract normal sinus rhythm beats from wrist-worn photoplethysmography recordings
Papini, G. B., Fonseca, P. M., Eerikäinen, L. M., Overeem, S., Bergmans, J. W. M., & Vullings, R. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aae7f8 · Cited by 32
Effect of changes in sympathovagal balance on the accuracy of heart rate variability obtained from photoplethysmography
Chen, X., HUANG, Y.-Y., Yun, F., Chen, T., & Li, J. (2015) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2015.2784 · Cited by 32
Reliability of Short-Term Heart Rate Variability Indexes Assessed through Photoplethysmography
Pernice, R., Javorka, M., Krohová, J., Czippelová, B., Turianiková, Z., Busacca, A., & Faes, L. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513634 · Cited by 31
“In the recent past, a number of studies have investigated the agreement between HRV and PRV, indicating that such an agreement varies depending on the considered descriptive measure of HRV and on the physiological condition (see [8] for a comprehensive review).”
Background
Speckleplethysmographic (SPG) Estimation of Heart Rate Variability During an Orthostatic Challenge
Dunn, C. E., Monroe, D. C., Crouzet, C., Hicks, J. W., & Choi, B. (2019) · Scientific Reports · doi:10.1038/s41598-019-50526-0 · Cited by 31
Validation of the Polar OH1 and M600 optical heart rate sensors during front crawl swim training
Olstad, B. H., & Zinner, C. (2020) · PLoS ONE · doi:10.1371/journal.pone.0231522 · Cited by 34
MS-Net: Sleep apnea detection in PPG using multi-scale block and shadow module one-dimensional convolutional neural network
Wei, K., Zou, L., Liu, G., & Wang, C. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106469 · Cited by 36
Predefined vs data‐guided training prescription based on autonomic nervous system variation: A systematic review
Düking, P., Zinner, C., Reed, J. L., Holmberg, H., & Sperlich, B. (2020) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.13802 · Cited by 34
Estimation of heart rate and heart rate variability from pulse oximeter recordings using localized model fitting
Wadehn, F., Carnal, D., & Loeliger, H. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319225 · Cited by 29
“Pulse oximetry is increasingly proving to be a useful tool for unobtrusive monitoring of essential physiological parameters such as the heart rate (HR) and the pulse rate variability (PRV), a surrogate measure for the heart rate variability (HRV), in both home-care and intensive care environments [1].”
Background
Don’t add or miss a beat: a guide to cleaner heart rate variability recordings.
Shaffer, F., & Combatalade, D. C. (2013) · Biofeedback · doi:10.5298/1081-5937-41.3.04 · Cited by 30
“The ECG allows a more accurate calculation of the interbeat interval, especially during psychological stressors (Schäfer & Vagedes, 2013).”
Background
Heart-brain synchronization breakdown in Parkinson’s disease
Iniguez, M., Jiménez-Marín, A., Erramuzpe, A., Acera, M., Tijero, B., Murueta‐Goyena, A., Pino, R. D., Fernández, T., Carmona‐Abellán, M., Cabrera‐Zubizarreta, A., et al. (2022) · npj Parkinson s Disease · doi:10.1038/s41531-022-00323-w · Cited by 35
Assessment of skin blood flow following spinal manual therapy: A systematic review
Zegarra-Parodi, R., Park, P. Y. S., Heath, D. M., Makin, I. R. S., Degenhardt, B. F., & Roustit, M. (2014) · Manual Therapy · doi:10.1016/j.math.2014.08.011 · Cited by 30
“The SSNA could be noninvasively assessed with pulse rate variability, a frequency-domain analysis of a PPG signal that is considered an accurate estimate of heart rate variability in quiet and controlled conditions (Schafer and Vagedes, 2013).”
Background
Respiratory sinus arrhythmia stabilizes mean arterial blood pressure at high-frequency interval in healthy humans
Elstad, M., Walløe, L., Holme, N. L. A., Maes, E., & Thoresen, M. (2014) · European Journal of Applied Physiology · doi:10.1007/s00421-014-3042-3 · Cited by 30
“Pulse rate is in good–moderate accordance with HR in healthy subjects at rest (Schafer and Vagedes 2013).”
Result
PhysioLab - a multivariate physiological computing toolbox for ECG, EMG and EDA signals: a case of study of cardiorespiratory fitness assessment in the elderly population
Muñoz, J. E., Gouveia, É. R., Cameirão, M. S., & Badia, S. B. I. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5069-z · Cited by 30
Heart rate response to cognitive load as a marker of depression and increased anxiety
Alshanskaia, E. I., Alexandrovna, Z. N., Поликанова, И. С., & Martynova, O. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1355846 · Cited by 34
“Thus, despite ECG being considered a more reliable method for measuring HRV (56), and the relative scarcity of research into PPG signals in relation to depression and anxiety, our results suggest that PPG is an effective tool for gauging depression and trait anxiety through HR measurement.”
Method
Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress
Posada-Quintero, H. F., Reljin, N., Moutran, A., Georgopalis, D., Lee, E. C., Giersch, G. E. W., Casa, D. J., & Chon, K. H. (2019) · Nutrients · doi:10.3390/nu12010042 · Cited by 29
“[18–20], with the advantage that photoplethysmography is a low-cost optical technique [21] that is much simpler to use than the electrocardiogram and is easily deployed in smartphones and wearable devices [22–24].”
Background
Estimation of heart rate variability from finger photoplethysmography during rest, mild exercise and mild mental stress
Singstad, B.-J., Azulay, N., Bjurstedt, A., Bjørndal, S. S., Drageseth, M. F., Engeset, P., Eriksen, K., Gidey, M. Y., Granum, E. O., Greaker, M. G., et al. (2021) · Journal of Electrical Bioimpedance · doi:10.2478/joeb-2021-0012 · Cited by 29
“Several studies have compared HRV derived from PPG with ECG-based HRV as a reference [8-10] and good agreement has generally been demonstrated for healthy subjects at rest, but moderate physical, and sometimes mental stress, tends to diminish the agreement [8,11,12].”
Background
Using imaging photoplethysmography for heart rate estimation in non-human primates
Михайлович, У. А., Moeller, S., Kagan, I., Gail, A., Treue, S., & Wolf, F. (2018) · PLoS ONE · doi:10.1371/journal.pone.0202581 · Cited by 29
Co-Located Multimodal Sensing: A Next Generation Solution for Wearable Health
Goverdovsky, V., Looney, D., Kidmose, P., Papavassiliou, C., & Mandic, D. P. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2338612 · Cited by 28
“PPG peaks is a good proxy for heart rate variability [21], and”
Background
Emotional metacognition: stimulus valence modulates cardiac arousal and metamemory
Legrand, N., Engen, S. S., Corrêa, C. M. C., Mathiasen, N. K., Nikolova, N. E., Fardo, F., & Allen, M. (2020) · Cognition & Emotion · doi:10.1080/02699931.2020.1859993 · Cited by 30
High natural killer cell number might identify stroke patients at risk of developing infections
Raedt, S. D., Vos, A. D., Vanbinst, A., Waele, M. D., Coomans, D. H., Buyl, R., & Keyser, J. D. (2015) · Neurology Neuroimmunology & Neuroinflammation · doi:10.1212/nxi.0000000000000071 · Cited by 28
“PRV derived from noninvasive blood pressure waveforms corresponds sufficiently with traditional HRV derived from ECG in subjects at rest.(10) PRV was calculated using Kubios software (University of Eastern Finland, Kuopio, Finland).”
Method
Correlation between RR, inter-systolic and inter-diastolic intervals and their differences for the analysis of spontaneous heart rate variability
Dias, D. P. M., Silva, L. E. V., Katayama, P. L., Silva, C. A. A., Salgado, H. C., & Fazan, R. (2016) · Physiological Measurement · doi:10.1088/0967-3334/37/7/1120 · Cited by 28
“Although RR has been considered the gold standard for HRV analysis (Russoniello et al 2013, Schäfer and Vagedes 2013), PI series are commonly used for this purpose (Di Rienzo et al 1996, Honzíková et al 2003, Silva et al 2015).”
Method
Validating EmotiBit, an open-source multi-modal sensor for capturing research-grade physiological signals from anywhere on the body
Montgomery, S. M., Nair, N., Chen, P., & Dikker, S. (2024) · Measurement Sensors · doi:10.1016/j.measen.2024.101075 · Cited by 28
PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring—A Comprehensive Review
Lapsa, D., Janeliukštis, R., Metshein, M., & Selavo, L. (2024) · Applied Sciences · doi:10.3390/app14177451 · Cited by 30
Effects of Capsinoid Intake on Brown Adipose Tissue Vascular Density and Resting Energy Expenditure in Healthy, Middle-Aged Adults: A Randomized, Double-Blind, Placebo-Controlled Study
Fuse, S., Endo, T., Tanaka, R., Kuroiwa, M., Ando, A., Kume, A., Yamamoto, A., Kuribayashi, K., Somekawa, S., Takeshita, M., et al. (2020) · Nutrients · doi:10.3390/nu12092676 · Cited by 27
Comparing apples and oranges or different types of citrus fruits? Using wearable versus stationary devices to analyze psychophysiological data
Konstantinou, P., Trigeorgi, A., Georgiou, C., Gloster, A. T., Panayiotou, G., & Karekla, M. (2020) · Psychophysiology · doi:10.1111/psyp.13551 · Cited by 27
“…recordings of only a few minutes are sufficient for the calculation of certain HRV parameters (Hamilton, Mckechnie, & Macfarlane, 2004; Migliaro, Canetti, Contreras, & Hakas, 2003; Schäfer & Vagedes, 2013) and this is why we decided to proceed with the HRV analysis during the experimental phase.”
Background
Maternal bonding in childhood moderates autonomic responses to distress stimuli in adult males
Dalsant, A., Truzzi, A., Setoh, P., & Esposito, G. (2015) · Behavioural Brain Research · doi:10.1016/j.bbr.2015.06.026 · Cited by 28
Pre-partum HRV as a predictor of postpartum depression: The potential use of a smartphone application for physiological recordings
Solorzano, C. S., Violani, C., & Grano, C. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.09.056 · Cited by 26
Heart Rate Variability Analysis: How Much Artifact Can We Remove?
Sheridan, D. C., Dehart, R., Lin, A. L., Sabbaj, M., & Baker, S. D. (2020) · Psychiatry Investigation · doi:10.30773/pi.2020.0168 · Cited by 26
Heart rate variability in late pregnancy: exploration of distinctive patterns in relation to maternal mental health
Kimmel, M., Fransson, E., Cunningham, J. L., Bränn, E., Grewen, K., Boschiero, D., Chrousos, G. P., Meltzer-Brody, S. E., & Skalkidou, A. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01401-y · Cited by 27
Effects of using different algorithms and fiducial points for the detection of interbeat intervals, and different sampling rates on the assessment of pulse rate variability from photoplethysmography
Mejía‐Mejía, E., May, J. M., & Kyriacou, P. A. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106724 · Cited by 26
Effects of Missing Data on Heart Rate Variability Metrics
Cajal, D., Hernando, D., Lázaro, J. M., Laguna, P., Gil, E., & Bailón, R. (2022) · Sensors · doi:10.3390/s22155774 · Cited by 26
“Despite pulse rate variability being different from HRV, it can be used as a surrogate in many practical situations [8,9].”
Background
Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach
Liu, I., Ni, S., & Peng, K. (2020) · Sensors · doi:10.3390/s20071923 · Cited by 26
“The two measurements are considered to have a good or acceptable agreement when the BAR is less than 10% or 20% [40, 66] .”
Background
Comparison of Wearable and Clinical Devices for Acquisition of Peripheral Nervous System Signals
Bizzego, A., Gabrieli, G., Furlanello, C., & Esposito, G. (2020) · Sensors · doi:10.3390/s20236778 · Cited by 25
“This result is expected as the beat detection in BVP signals is in general more difficult due to posture and individual physiological characteristics [26].”
Background
Screening for Major Depressive Disorder Using a Wearable Ultra-Short-Term HRV Monitor and Signal Quality Indices
Sato, S., Hiratsuka, T., Hasegawa, K., Watanabe, K., Obara, Y., Kariya, N., Shinba, T., & Matsui, T. (2023) · Sensors · doi:10.3390/s23083867 · Cited by 25

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)
DOI: 10.1016/j.ijcard.2012.03.119
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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How accurate is pulse rate variability as an estimate of heart rate variability?

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