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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Alaerts, K., Taillieu, A., Prinsen, J., & Daniëls, N. (2021) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2021.105397 · Cited by 9
ACUTE TRANSCRANIAL DIRECT CURRENT STIMULATION (tDCS) IMPROVES VENTILATORY VARIABILITY AND AUTONOMIC MODULATION IN RESISTANT HYPERTENSIVE PATIENTS
Ministro, G., Castaño, J. B., Barboza, C. D. A., Moura, E. G., Ferreira‐Melo, S. E., Mostarda, C. T., Fattori, A., Moreno, H., & Rodrigues, B. (2021) · Respiratory Physiology & Neurobiology · doi:10.1016/j.resp.2021.103830 · Cited by 9
Examining weekly heart rate variability changes: a comparison between monitoring methods
Ruiz‐Alias, S. A., Marcos‐Blanco, A., Clavero‐Jimeno, A., & García‐Pinillos, F. (2022) · Sports Engineering · doi:10.1007/s12283-022-00371-8 · Cited by 12
Workload management through glanceable feedback: The role of heart rate variability
Muñoz, J. E., Pereira, F., & Karapanos, E. (2016) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2016.7749477 · Cited by 8
Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications
Vila, G., Godin, C., Charbonnier, S., & Campagne, A. (2021) · Sensors · doi:10.3390/s21165357 · Cited by 8
“However, HRV parameters have also shown substantial deviations between PPG and ECG under the inﬂuence of moderate mental effort or wrist activity [14].”
Method
Camera-Based Heart Rate Variability for Estimating the Maturity of Neonatal Autonomic Nervous System
Liao, C., Zeng, Y., Tan, C., Yu, D., Song, X., Pan, L., He, C., Wang, Y., Yang, J., Lu, H., et al. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3439524 · Cited by 8
“Besides, some studies found that short-term variability is also more susceptible to the disagreement between HRV measured by ECG and PPG, due to the fact that PPG estimates cardiac status through peripheral circulation [60].”
Background
Test–Retest Reliability and Concurrent Validity of Photoplethysmography Finger Sensor to Collect Measures of Heart Rate Variability
Rogers, D. W., Himariotis, A. T., Sherriff, T. J., Proulx, Q. J., Duong, M. T., Noel, S. E., & Cornell, D. J. (2025) · Sports · doi:10.3390/sports13020029 · Cited by 9
Design of Emotion-Driven Game Interaction Using Biosignals
Frachi, Y., Takahashi, T., Wang, F., & Barthet, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05637-6_10 · Cited by 9
Impact of personal protective equipment on wearer's health indicators using multi-parameter physiological monitoring
Jia, Z., Bekö, G., Zhang, Z., Zhong, L., & Ai, Z. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111862 · Cited by 8
A multispectral testbed for cardiovascular sensing using imaging photoplethysmography
Blackford, E. B., & Estepp, J. R. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2253447 · Cited by 8
Building IoT-Enabled Wearable Medical Devices: An Application to a Wearable, Multiparametric, Cardiorespiratory Sensor
Gatouillat, A., Massot, B., Badr, Y., Sejdić, E., & Géhin, C. (2018) · International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) · doi:10.5220/0006729101090118 · Cited by 8
“Although widely used, the accuracy of other popular methods such as plethysmography is still questioned by the community (Schäfer and Vagedes, 2013) and suffer from heavy movement artifact.”
Initialization of Pulse Transit Time-Based Blood Pressure Monitors
Mukkamala, R., & Hahn, J. (2019) · Springer eBooks · doi:10.1007/978-3-030-24701-0_10 · Cited by 8
The effect of using activity workstations on heart rate variability during complex cognitive tasks
Pilcher, J. J., Morris, D. M., Limyansky, S. E., & Bryant, S. A. (2020) · Journal of American College Health · doi:10.1080/07448481.2020.1782919 · Cited by 9
“Although this method is commonly seen in the literature, it is an indirect measure linked to the sympathovagal balance.(69) LF HRV has been the center of several debates about the use of HRV, albeit it is often the best method available for the noninvasive measure of cardio sympathetic reactivity and autonomic balance.”
Method
A New Sleep Staging System for Type III Sleep Studies Equipped With a Tracheal Sound Sensor
Vanbuis, J., Feuilloy, M., Baffet, G., Meslier, N., Gagnadoux, F., & Girault, J. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3120927 · Cited by 8
Pulse rate variability: An alternative to heart rate variability in adults with spinal cord injury
Schoffl, J., Pozzato, I., Rodrigues, D., Arora, M., & Craig, A. R. (2023) · Psychophysiology · doi:10.1111/psyp.14356 · Cited by 8
Validity of the polar verity sense during swimming at different locations and intensities
López‐Belmonte, Ó., Febles‐Castro, A., Gay, A., Cuenca‐Fernández, F., Arellano, R., & Ruiz-Navarro, J. J. (2023) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.14494 · Cited by 8
“6 The different results are likely related to environmental influences rather than device malfunction, 7,8 as several issues could arise due to the aquatic environment.”
Background
Evaluation of agreement between temporal series obtained from electrocardiogram and pulse wave.
Leikan, G. M., Rossi, E., Sanz, M., Delisle-Rodríguez, D., Mantaras, M. C., Nicolet, J., Zapata, D., Lapyckyj, I., Siri, L. N., & Perrone, M. (2016) · Journal of Physics Conference Series · doi:10.1088/1742-6596/705/1/012038 · Cited by 8
A methodology to quantify the differences between alternative methods of heart rate variability measurement
Garcia-Gonzalez, M. A., Fernández-Chimeno, M., Guede-Fernández, F., Ferrer-Mileo, V., Argelagos-Palau, A., Álvarez-Gómez, L., Parrado, E., Moreno, J., Capdevila, L., & Ramos-Castro, J. J. (2015) · Physiological Measurement · doi:10.1088/0967-3334/37/1/128 · Cited by 8
Use of photoplethysmography to predict mortality in intensive care units
Kock, K. D. S., & Marques, J. L. B. (2018) · Vascular Health and Risk Management · doi:10.2147/vhrm.s172643 · Cited by 8
“36,37 In this study, this measure was taken excluding the ectopic beats and signal noises, for only 1 minute.”
Effect of urban environment on cardiovascular health: a feasibility pilot study using machine learning to predict heart rate variability in patients with heart failure
van Es, V. A. A., Lathauwer, I. L. J. D., Lopata, R. G. P., Kemperman, A. D. A. M., Dongen, R. P. V., Brouwers, R. W. M., Funk, M., & Kemps, H. (2024) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztae050 · Cited by 8
“20,21 The automatic beat correction algorithm within the Kubios HRV tool was utilized to correct beats.”
Method
A Multi-Layered Origami Tactile Sensory Ring for Wearable Biomechanical Monitoring
Karmakar, R. S., Lin, H.-F., Huang, J., Chao, J., Liao, Y., & Lu, Y. (2024) · Biosensors · doi:10.3390/bios15010008 · Cited by 8
The Validation and Accuracy of Wearable Heart Rate Trackers in Children With Heart Disease: Prospective Cohort Study
Hardon, H. J., Kerkhof, Y. N. V., Bartelds, B., Kammeraad, J. A. E., & van Deutekom, A. W. (2025) · JMIR Formative Research · doi:10.2196/70835 · Cited by 9
Photoplethysmography-Based Pulse Rate Variability and Haemodynamic Changes in the Absence of Heart Rate Variability: An In-Vitro Study
Mejía‐Mejía, E., & Kyriacou, P. A. (2022) · Applied Sciences · doi:10.3390/app12147238 · Cited by 7
Psychophysiological assessment of trauma-focused Group Music and Imagery therapy for women with PTSD or CPTSD using script-driven imagery. A randomised controlled study.
Rudstam, G., Elofsson, U. O. E., Söndergaard, H. P., & Beck, B. D. (2023) · European Journal of Trauma & Dissociation · doi:10.1016/j.ejtd.2023.100353 · Cited by 8
Effects of posture on heart rate variability in non-frail and prefrail individuals: a cross-sectional study
Chen, H., Tse, M. Y. M., Chung, J. W. Y., Yau, S. Y., & Wong, T. K. S. (2023) · BMC Geriatrics · doi:10.1186/s12877-023-04585-8 · Cited by 9
Comparisons in heart rate readings between the Bioconnected wireless exercise earpiece and a Polar T31-Coded Chest Strap during a GXT
Higgins, T. R., Lloyd, T., Manalansan, K., McDonald, S., & Thomas, B. (2018) · Journal of Human Sport and Exercise · doi:10.14198/jhse.2018.133.05 · Cited by 7
“The results of this study further demonstrate PPG as a technological advancement which delivers accurate HR monitoring, which Schafer and Vagedes (2013) have previously highlighted as a valid form of measuring HR.”
Method
Comparison Between Heart Rate Variability and Pulse Rate Variability for Bradycardia and Tachycardia Subjects
Chou, Y., Zhu, P., Huang, X., Lin, J., Liu, J., & Gu, Y. (2018) · International Conference on Control, Automation and Information Sciences (ICCAIS) · doi:10.1109/iccais.2018.8570697 · Cited by 7
Compliance of self-measured HRV using smartphone applications in collegiate athletes
Holmes, C. J., Sherman, S. R., Hornikel, B., Cicone, Z. S., Wind, S. A., & Esco, M. R. (2020) · The Journal of High Technology Management Research · doi:10.1016/j.hitech.2020.100376 · Cited by 7
Herzfrequenzvariabilitätsanalyse in der betriebsärztlichen Praxis
Böckelmann, I., & Sammito, S. (2020) · Zentralblatt für Arbeitsmedizin Arbeitsschutz und Ergonomie · doi:10.1007/s40664-020-00401-x · Cited by 7
Combination of ECG And PPG Signals For Smart HealthCare Systems: Techniques, Applications, and Challenges
Bassiouni, M. M., Hegazy, I., Rizk, N. J., El-Dahshan, E.-S. A., & Salem, A.-B. M. (2021) · International Conference on Intelligent Computing and Information Systems (ICICIS) · doi:10.1109/icicis52592.2021.9694241 · Cited by 7
Combination of ECG and PPG Signals for Healthcare Applications: A Survey
Bassiouni, M. M., Hegazy, I., Rizk, N. J., El-Dahshan, S. A., & Salem, A.-B. M. (2021) · Advances in Modelling and Analysis B · doi:10.18280/ama_b.641-409 · Cited by 7
“It is considered to be the nearest alternative to the ECG signal as it holds essential cardiovascular information [5, 6].”
Background
Validation of a smart shirt for heart rate variability measurements at rest and during exercise
Schams, P., Feodoroff, B., Zacher, J., Eibl, A., & Froböse, I. (2022) · Clinical Physiology and Functional Imaging · doi:10.1111/cpf.12746 · Cited by 8
“We excluded record segments from further statistical analysis with more than 10% of beat errors (Schäfer & Vagedes, 2013) as well as their paired record segment obtained by the other device.”
Method
Cardiovascular Autonomic Responses to Aerobic, Resistance and Combined Exercises in Resistance Hypertensive Patients
Pires, N. F., Coelho‐Júnior, H. J., Gambassi, B. B., de Faria, A. P., Ritter, A. M. V., Gasparetti, C. S., Pioli, M. R., Ruberti, O. M., Ferreira‐Melo, S. E., Moreno, H., et al. (2022) · BioMed Research International · doi:10.1155/2022/8202610 · Cited by 8
“Although the PI variability assessment may be considered less accurate than measuring heart rate variability by electrocardiogram, some studies have demonstrated the agreement between heart rate variability and PI variability [26, 27].”
Automated analysis of finger blood pressure recordings provides insight in determinants of baroreflex sensitivity and heart rate variability—the HELIUS study
Collard, D., Westerhof, B. E., Karemaker, J. M., Stok, W. J., Postema, P. G., Krediet, C. T. P., Vogt, L., & van den Born, B. H. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02768-4 · Cited by 9
Towards a physiology based difficulty control system for serious games
Köles, M., Szegletes, L., & Forstner, B. (2015) · IEEE International Conference on Cognitive Infocommunications · doi:10.1109/coginfocom.2015.7390612 · Cited by 7
“Data quality will be an issue, as it is with current photoplethysmography based technologies [26], but the potential of widespread application in everyday life make cardiac measures ideal candidates for our system.”
Background
Remote detection of photoplethysmographic signal and SVM based classification
Kalkhaire, S. D., & Puranik, V. G. (2016) · IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT) · doi:10.1109/icaecct.2016.7942568 · Cited by 7
A novel approach for comparison of heart rate variability derived from synchronously measured electrocardiogram and photoplethysmogram
Lekkala, R. G. R., & Kuntamalla, S. (2017) · IEEE Healthcare Innovations and Point of Care Technologies Conference (HI-POCT) · doi:10.1109/hic.2017.8227599 · Cited by 7
Multiscale entropy and poincare plot-based analysis of pulse rate variability and heart rate variability of ICU patients
Parasnis, R., Pawar, A. B., & Manivannan, M. (2015) · International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) · doi:10.1109/iciibms.2015.7439531 · Cited by 7
“As for MSE, none of the 105 studies that were reviewed in [14] includes any entropy measure computed over multiple temporal scales.”
Background
Quantification of error between the heartbeat intervals measured form photoplethysmogram and electrocardiogram by synchronisation
Kuntamalla, S., & Lekkala, R. G. R. (2018) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2018.1513578 · Cited by 7
Detection of Daily Emotions by Wearable Biometric Sensors
Yuda, E., Tanabiki, T., Iwata, S., Abe, K., & Hayano, J. (2019) · IEEE Global Conference on Life Sciences and Technologies (LifeTech) · doi:10.1109/lifetech.2019.8883968 · Cited by 7
“Second, the shape of the peaks changes with stroke volume, blood pressure, pulse wave velocity and the pressure reflection from peripheral vasculatures, which are affected by body posture, respiration, and emotions [3].”
Background
Validation of the EmotiBit wearable sensor for heart-based measures under varying workload conditions
Vorreuther, A., Tagalidou, N., & Vukelić, M. (2025) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2025.1585469 · Cited by 10
Assessing pedestrian stress with biometric sensing and survey responses
Ma, S., Zhang, W., Noland, R. B., Andrews, C. J., Younes, H., & Hagen, L. A. V. (2025) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2025.103347 · Cited by 7
Heart rate variability and its modulation by nutrients: a narrative review on implications for cardiovascular aging
Zhao, Y., Chen, P., Zhang, Y., Huo, S., Yu, D., Zeng, X., & Zhang, W. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1654796 · Cited by 6

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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