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, it was reported that PRV could be differ from HRV under dynamic conditions, such as exercise or mental stress conditions (Schäfer and Vagedes, 2013; Mejía-Mejía et al., 2020).”
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“Such methodological differences between PPG and ECG can explain why PPG and ECG findings are comparable during rest, but are not comparable during stress, for example (Schäfer and Vagedes, 2013).”
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“The ECG method is more accurate than PPG during paced breathing (Jan et al., 2019) and when increased sympathetic tone results in vasoconstriction in monitored fingers (Giardino et al., 2002; Schafer and Vagedes, 2013).”
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“In the absence of excessive movement, the signal from pulse oximetry is comparable to that from the ECG (Schäfer & Vagedes, 2013).”
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The 2023 wearable photoplethysmography roadmap
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“The main challenge is sensitivity to motion, and studies have illustrated more noisy estimates of HRV with PPG than those measured with ECG, particularly in high-frequency parameters (Schäfer and Vagedes 2013).”
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“However, it should be noted that such devices measure variations in pulse rate rather than HRV directly (Schäfer and Vagedes, 2013).”
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“Several studies focused on the analogy between PRV and HRV, leading to the result that both are linked to the ANS activity, and that the same features can be extracted from the two signals [5].”
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“Nonetheless, although PRV has been treated as a valid surrogate of HRV, their relationship is not entirely clear, and PRV has been found to significantly differ from HRV under certain circumstances (Schäfer and Vagedes, 2013).”
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BackgroundResultInfluentialStressing the accuracy: Wrist‐worn wearable sensor validation over different conditions
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“It has been demonstrated that PRV is an accurate estimator of HRV when applied to healthy subjects at rest (Schäfer and Vagedes, 2013), a requirement that is satisfied in our study.”
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Autonomic dysfunction in acute ischemic stroke: An underexplored therapeutic area?
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“Alternatively, interbeat intervals (ibi) might be calculated from non-invasive blood pressure monitoring using plethysmography (eg NEXFIN HD®, WatchPAT®, POLAR®, Finometer®, CNAPTM®, Finapres®), which is called pulse rate variability [15–17].”
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“Although few studies have assessed clinical and older populations, pulse rate variability offers an accurate estimate of HRV at rest in healthy adults (73).”
BackgroundMethodGraphene coated textile based highly flexible and washable sports bra for human health monitoring
Shathi, M. A., Chen, M., Khoso, N. A., Rahman, M. T., & Bhattacharjee, B. (2020) · Materials & Design · doi:10.1016/j.matdes.2020.108792 · Cited by 126
A deep transfer learning approach for wearable sleep stage classification with photoplethysmography
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“As mentioned before, these can be caused by, for example, the known discrepancy in hemodynamic or cardiovascular physiology between HRV data measured from ECG and PPG signals and for assessing autonomic responses 39,40 , and a higher sensitivity of wrist-worn PPG to motion artefacts than ECG 41 .”
BackgroundCardiovascular assessment by imaging photoplethysmography – a review
Zaunseder, S., Trumpp, A., Wedekind, D., & Malberg, H. (2018) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2017-0119 · Cited by 121
Heart rate variability in bipolar disorder: A systematic review and meta-analysis
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A Practical Guide to Resonance Frequency Assessment for Heart Rate Variability Biofeedback
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“The difference between the ECG/IBI- and PPG/IPI-derived cardiac cycle rates served as a best-case baseline comparison to calculating heart rate from ECG using rates based on individual cycle identification to create IXI time series, as cardiac cycle rates derived from ECG and PPG should not a priori assumed to be equa…”
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“Investigation of photoplethysmography (PPG) accuracy as an estimate of HRV and the possibility of employing it as a surrogate of electrocardiography (ECG) have been the topics of many experimental and survey studies [19], [21], [55], [56].”
BackgroundWearable Photoplethysmography for Cardiovascular Monitoring.
Charlton, P. H., Kyriacou, P. A., Mant, J., Marozas, V., Chowienczyk, P., & Alastruey, J. (2022) · KTUePubl (Repository of Kaunas University of Technology) · doi:10.17863/cam.81148 · Cited by 113
A literature review of heart rate variability in depressive and bipolar disorders
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“HRV is also highly sensitive to respiration, including respiratory rate, respiratory depth and respiratory sinus arrhythmia (RSA) (Heathers, 2014; Perakakis et al., 2009; Quintana and Heathers, 2014; Schafer and Vagedes, 2013; Shaffer et al., 2014).”
BackgroundAn Open Framework for Remote-PPG Methods and Their Assessment
Boccignone, G., Conte, D., Cuculo, V., D’Amelio, A., Grossi, G., & Lanzarotti, R. (2020) · IEEE Access · doi:10.1109/access.2020.3040936 · Cited by 109
“This fact is witnessed by a number of publications reporting universally good agreement between PRV and HRV, even if this concordance may be susceptible of many variables such as experimental conditions and postures (see [48] and the citations thereof).”
BackgroundMethodZhang, Y., Liu, B., & Zhang, Z. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2015.05.006 · Cited by 109
Cho, Y., Julier, S., & Bianchi‐Berthouze, N. (2018) · JMIR Mental Health · doi:10.2196/10140 · Cited by 109
“In the case of PPG, the term Pulse Rate Variability (PRV) or PPG HRV are often used to clarify the different type (even if related) of event measured [26,29–31] with respect to ECG.”
MethodA Review of Deep Learning-Based Contactless Heart Rate Measurement Methods
Ni, A., Azarang, A., & Kehtarnavaz, N. (2021) · Sensors · doi:10.3390/s21113719 · Cited by 109
“Contact ECG and PPG [19] 2012 Accuracy of pulse rate variability (PRV) as an estimate of HRV”
BackgroundMethodRemote Healthcare for Elderly People Using Wearables: A Review
Olmedo-Aguirre, J. O., Reyes-Campos, J., Alor‐Hernández, G., Machorro-Cano, I., Rodríguez-Mazahua, L., & Sánchez-Cervantes, J. L. (2022) · Biosensors · doi:10.3390/bios12020073 · Cited by 121
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“Seven electrodes were placed on the teacher’s chest to minimize the influence of movement (Porges & Byrne, 1992) and to get a more reliable estimate of heart rate than with wristbands based on pulse plethysmography (PPG; Rohrbaugh, 2016; Schäfer & Vagedes, 2013).”
MethodEvaluation of the beat-to-beat detection accuracy of PulseOn wearable optical heart rate monitor
Parák, J., Tarniceriu, A., Renevey, P., Bertschi, M., Delgado-Gonzalo, R., & Korhonen, I. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7320273 · Cited by 99
“However, it has been suggested that pulse rate variability (PRV) from PPG is sufficiently accurate only for healthy (and mostly younger) subjects at rest, HRV estimated from PRV tends to be overestimated against ECG based values, and motion artifacts lead to inaccurate PPGbased beat detection [15].”
BackgroundSammito, S., Thielmann, B., Klußmann, A., Deußen, A., Braumann, K.-M., & Böckelmann, I. (2024) · Journal of Occupational Medicine and Toxicology · doi:10.1186/s12995-024-00414-9 · Cited by 116
“A systematic review [24] showed that only in young, healthy subjects under resting conditions are there acceptable correspondences between NN intervals and pulse NN intervals, but HRV parameters based on the pulse NN intervals are sometimes significantly increased (e.g. in HF).”
BackgroundYe, Y., Cheng, Y., He, W., Hou, M., & Zhang, Z. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2597265 · Cited by 94
“Since rhythms of PPG signals correspond to heartbeats, estimated HR from PPG signals can be achieved by detecting instantaneous fundamental frequency of PPG signals [6], [7].”
MethodAn Ultra Low Power Personalizable Wrist Worn ECG Monitor Integrated With IoT Infrastructure
Beach, C., Krachunov, S., Pope, J., Fafoutis, X., Piechocki, R. J., Craddock, I., & Casson, A. J. (2018) · IEEE Access · doi:10.1109/access.2018.2864675 · Cited by 91
“For PPG signals, we note that variability measures are typically termed Pulse Rate Variability (PRV) [60] to account for the minor differences between HRV and PRV as the pulse transit time down the arm varies with posture and blood pressure.”
BackgroundMethodInfluentialPapini, G. B., Fonseca, P., van Gilst, M. M., Bergmans, J. W. M., Vullings, R., & Overeem, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-69935-7 · Cited by 90
Hohenschurz‐Schmidt, D., Calcagnini, G., Dipasquale, O., Jackson, J. B., Medina, S., O’Daly, O., O’Muircheartaigh, J., Rubio, A. D. L., Williams, S., McMahon, S. B., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00147 · Cited by 91
“Sufficient accuracy under stationary conditions has also been confirmed by a review on the topic (Schäfer and Vagedes, 2013).”
BackgroundMethodSarkar, P., & Etemad, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16126 · Cited by 88
Zhang, X., Kou, W., Chang, E., Gao, H., Fan, Y., & Xu, Y. L. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.10.010 · Cited by 91
Sensors Capabilities, Performance, and Use of Consumer Sleep Technology
de Zambotti, M., Cellini, N., Menghini, L., Sarlo, M., & Baker, F. C. (2020) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2019.11.003 · Cited by 95
Comparison of Polar M600 Optical Heart Rate and ECG Heart Rate during Exercise
HORTON, J. F., STERGIOU, P., FUNG, T. S., & KATZ, L. (2017) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001388 · Cited by 79
Михайлович, У. А. (2018) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/aabd09 · Cited by 78
“…from electrocardiogram or assessed by pulse rate (number of pulse waves transmitted from heart to periphery per unit of time, usually highly correlated with heart rate (Schäfer and Vagedes 2013))which can be computed from indirect measurements such as photoplethysmogram (PPG, (Allen 2007).”
BackgroundMethodInfluentialWeiler, D. T., Villajuan, S. O., Edkins, L., Cleary, S., & Saleem, J. J. (2017) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213601804 · Cited by 87
“In fact, the usefulness of heart rate variability as a clinical diagnostic and research tool has previously been verified (Schafer & Vagedes, 2013).”
MethodInfluentialMayo, O., Lavidor, M., & Gordon, I. (2021) · Physiology & Behavior · doi:10.1016/j.physbeh.2021.113391 · Cited by 73
Shilton, A. L., Laycock, R., & Crewther, S. G. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00567 · Cited by 76
Ding, Z., Li, W., Wang, W., Zhao, Z., Zhu, Y., Hou, B., Zhu, L., Chen, M., & Che, L. (2024) · Advanced Science · doi:10.1002/advs.202309407 · Cited by 76
A scoping review of heart rate variability in sport and exercise psychology
Mosley, E., & Laborde, S. (2022) · International Review of Sport and Exercise Psychology · doi:10.1080/1750984x.2022.2092884 · Cited by 85
Vescio, B., Salsone, M., Gambardella, A., & Quattrone, A. (2018) · Sensors · doi:10.3390/s18030844 · Cited by 73
Bruehl, S. P., Olsen, R. B., Tronstad, C., Sevre, K., Burns, J. W., Schirmer, H., Nielsen, C. S., Stubhaug, A., & Rosseland, L. A. (2017) · Pain · doi:10.1097/j.pain.0000000000001070 · Cited by 73
“intervals reflected in both measures, R-R intervals can be estimated using pulse wave data obtained by plethysmography in the course of acquiring continuous noninvasive beat-to-beat BPs.(50) A systematic review showed that most investigations demonstrated good agreement between HRV estimates based on”
BackgroundMethodInfluentialEffects of slow breathing rate on blood pressure and heart rate variabilities
Chang, Q., Liu, R., & Shen, Z. (2013) · International Journal of Cardiology · doi:10.1016/j.ijcard.2013.08.121 · Cited by 66
Scardulla, F., D’Acquisto, L., Colombarini, R., Hu, S., Pasta, S., & Bellavia, D. (2020) · Sensors · doi:10.3390/s20185052 · Cited by 72
Valenza, G., Passamonti, L., Duggento, A., Toschi, N., & Barbieri, R. (2020) · Journal of The Royal Society Interface · doi:10.1098/rsif.2019.0878 · Cited by 77
Shilaih, M., Clerck, V. D., Falco, L., Kübler, F., & Leeners, B. (2017) · Scientific Reports · doi:10.1038/s41598-017-01433-9 · Cited by 69
Analysis of a Pulse Rate Variability Measurement Using a Smartphone Camera
Bánhalmi, A., Borbás, J., Fidrich, M., Bilicki, V., Gingl, Z., & Rudas, L. (2018) · Journal of Healthcare Engineering · doi:10.1155/2018/4038034 · Cited by 69
“PRV and HRV have good correlations, and it was con fi rmed by several previous publications [9 – 11].”
BackgroundResultSelf-Compassion and Psycho-Physiological Recovery From Recalled Sport Failure
Ceccarelli, L. A., Giuliano, R. J., Glazebrook, C. M., & Strachan, S. M. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.01564 · Cited by 85
“It should also be noted that here we used photoplethysmography as a proxy for electrocardiogram activity, and although these two measures may diverge under conditions of acute stress (Schäfer and Vagedes, 2013), photoplethysmography has been demonstrated to reliably assess acute stress reactivity (Charlton et al.”
MethodAccuracy of heart rate variability estimated with reflective wrist-PPG in elderly vascular patients
Antink, C. H., Mai, Y., Peltokangas, M., Leonhardt, S. K., Oksala, N., & Vehkaoja, A. T. (2021) · Scientific Reports · doi:10.1038/s41598-021-87489-0 · Cited by 66
Iozzia, L., Cerina, L., & Mainardi, L. T. (2016) · Physiological Measurement · doi:10.1088/0967-3334/37/11/1934 · Cited by 66
“[23] on the accuracy of PRV as an estimate of HRV.”
BackgroundResultMember, I., Pernice, R., Javorka, M., Krohová, J., Czippelová, B., Turianiková, Z., Busacca, A., & Faes, L. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-01957-4 · Cited by 66
Quantitative detection of sleep apnea with wearable watch device
Hayano, J., Yamamoto, H., Nonaka, I., Komazawa, M., Itao, K., Ueda, N., 田中, 春., & Yuda, E. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237279 · Cited by 67
Jan, H.-Y., Chen, M.-F., Fu, T., Lin, W., Tsai, C.-L., & Lin, K. (2019) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-019-00468-9 · Cited by 69
“We found that the PPGderived PR from either limb was an acceptable surrogate of ECG-derived HR, as reported in the literature [10, 11, 24, 33].”
BackgroundResultInfluentialReduction of Periodic Motion Artifacts in Photoplethysmography
Wijshoff, R. W. C. G. R., Mischi, M., & Aarts, R. M. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2553060 · Cited by 66
“This may result from residual motion artifacts, or from physiological fluctuations in IBIs during walking caused by variations in pre-ejection time and pulse transit time [22].”
Shin, H. (2015) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-015-9798-0 · Cited by 63
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“The ECG was measured using a SMOTE PSG device (Compumedics Ltd., Australia) at a sampling rate of 200Hz [24].”
BackgroundMethodKoenig, N., Seeck, A., Eckstein, J., Mainka, A., Huebner, T. A., Voss, A., & Weber, S. (2016) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2015.0212 · Cited by 60
“In the studies reviewed by Scha¨fer and Vagedes, 10 correlation values of R ‡ 0.9 were achieved for all HRV parameters when comparing HRV analysis by plethysmography with ECG recordings.”
InfluentialThe accuracy of heartbeat detection using photoplethysmography technology in cardiac patients
Blok, S., Piek, M. A., Tulevski, I. I., Somsen, G. A., & Winter, M. M. (2021) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2021.06.009 · Cited by 59
Towards Photoplethysmography-Based Estimation of Instantaneous Heart Rate During Physical Activity
Jarchi, D., & Casson, A. J. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2668763 · Cited by 58
“have shown that despite these minor differences PRV can be a useful measure of instantaneous heart rate [19]–[21], but it has not been possible to extract measures of instantaneous heart”
BackgroundPietilä, J., Mehrang, S., Tolonen, J., Helander, E. E., Jimison, H. B., Misha Pavel, C., & Korhonen, I. (2017) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-981-10-5122-7_37 · Cited by 59
Estimation of Mental Distress from Photoplethysmography
Zangróniz, R., Martínez‐Rodrigo, A., López, M. T., Pastor, J. M., & Fernández‐Caballero, A. (2018) · Applied Sciences · doi:10.3390/app8010069 · Cited by 59
“However, a high correlation between both metrics has been reported for subjects at rest [20].”
BackgroundTasmurzayev, N., Amangeldy, B., Иманбек, Б., Baigarayeva, Z., Imankulov, T., Dikhanbayeva, G., Amangeldi, I., & Sharipova, S. (2025) · Sensors · doi:10.3390/s25175272 · Cited by 62
“…strategies address this by first applying timestamp normalization to a common reference clock, often using the ECG R-wave peaks as temporal anchors for resampling lower-frequency signals such as PPG or respiration to physiologically relevant intervals (e.g., RR-interval alignment) [41].”
Blackford, E. B., & Estepp, J. R. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2083940 · Cited by 58
Weinschenk, S. W., Beise, R. D., & Lorenz, J. (2016) · European Journal of Applied Physiology · doi:10.1007/s00421-016-3401-3 · Cited by 56
“In accordance with other studies [for review, see (Schafer and Vagedes 2013)], HRV parameters reflecting long-term variability, such as mean-HR, SD2, and SDNN, agreed better than parameters reflecting the highly dynamic short-term changes of the heart rate (e.g., SD1, Power HF band, and RMSSD).”
BackgroundMethodResultInfluential
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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