Agreement analysis of heart rate variability indices at two different sampling rates for monitoring applications.

Authors

  • Eduardo San Roman Instituto de Medicina Traslacional e Ingenieria Biomedica (IMTIB); Universidad Hospital Italiano Buenos Aires (UHIBA) https://orcid.org/0000-0002-9369-7841
  • Javier Zelechower Facultad Buenos Aires, Universidad Tecnológica Nacional , Buenos Aires, Argentina https://orcid.org/0000-0003-2729-1541
  • Jose Manuel Gallardo Instituto de Medicina Traslacional e Ingenieria Biomedica (IMTIB); Universidad Hospital Italiano Buenos Aires (UHIBA) and Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) https://orcid.org/0000-0002-3412-7478
  • Marcelo Risk Instituto de Medicina Traslacional e Ingeniería Biomédica (Hospital Italiano), a la Universidad del Hospital Italiano y al Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina. https://orcid.org/0000-0003-0107-2551

Keywords:

HRV, ICU, biomarkers, indices

Abstract

Beat-to-beat variations in heart rate lead to heart rate variability (HRV), analysed from electrocardiogram or photoplethysmography signals, forming a non-equispaced time series of beats, which requires a resampling of 3 Hz or 4 Hz, for analysis in the frequency domain. HRV is considered a biomarker, predictor of the evolution of diseases in intensive care units (ICU). To enhance these HRV studies, it is necessary to monitor the patient’s health using portable devices, from admission to the ICU until discharge from it and subsequently at home. This requires monitoring devices that can minimise energy consumption and data storage. Reducing the sampling frequency in HRV can reduce energy consumption, computing power and to limite data storage. Therefore, the objective of this work is to prove that a series resampled at 1 Hz allows obtaining HRV indices, equivalent to a 3 Hz. Through concordance analysis, using a database of subjects with pharmacological autonomic blockade and postural changes. The results show equivalences between the indices, standard deviation (SDNN), total spectral power (PT), low frequency (LF) and long-term variability (SD2) and agree with those reported as predictors. This study has limitations, since only a small number of young men participated. Future studies should consider this. The reduction of SDNN, PT, LF values would be predictors of mortality in hospitals, so the equivalence found from series with 1Hz resampling, would allow the use of portable devices with optimized performance, to monitor the evolution of the disease in patients in ICUs.

Downloads

Download data is not yet available.

Author Biographies

Eduardo San Roman, Instituto de Medicina Traslacional e Ingenieria Biomedica (IMTIB); Universidad Hospital Italiano Buenos Aires (UHIBA)

Eduardo San Roman is a doctor specializing in Critical Care Medicine. Currently, he is Honorary Head of the Adult Intensive Care Service at the Hospital Italiano de Buenos Aires (Hospital Affiliated to the Join Commission International). In addition, he works as a researcher and professor at the Instituto de Medicina Traslacional e Ingenieria Biomedica (IMTIB). Dr. Eduardo San Roman is director of the Comité de Big Data e Inteligencia Artificial de la Sociedad Argentina de Terapia Intensiva.

Javier Zelechower, Facultad Buenos Aires, Universidad Tecnológica Nacional , Buenos Aires, Argentina

Javier Zelechower is a Electronics Engineer from the Universidad Tecnologica Nacional, Facultad Buenos Aires (FRBA-UTN), where he teaches control theory. He is also PhD student in Engineering at FRBA-UTN.

Jose Manuel Gallardo, Instituto de Medicina Traslacional e Ingenieria Biomedica (IMTIB); Universidad Hospital Italiano Buenos Aires (UHIBA) and Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)

Jose Manuel Gallardo is a professor at the undergraduate and graduate levels and researcher at the Universidad Hospital Italiano Buenos Aires (UHIBA), the Hospital Italiano de Buenos Aires and at the Facultad Haedo of the Universidad Tecnológica Nacional (FRH-UTN), Argentina. He is also a Senior Professional at the Instituto de Medicina Traslacional e Ingeniería Biomédica (IMTIB) belonging to the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). He holds a PhD in Engineering, a Master's degree in Biomedical Engineering, a postgraduate degree in Higher Education and is an Electronics Engineer.

Marcelo Risk, Instituto de Medicina Traslacional e Ingeniería Biomédica (Hospital Italiano), a la Universidad del Hospital Italiano y al Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.

Marcelo Risk is currently Director of the Instituto de Medicina Traslacional e Ingeniería Biomédica (IMTIB), associated with the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Hospital Italiano de Buenos Aires (UHIBA) and the Hospital Italiano de Buenos Aires, Argentina, Principal Investigator at CONICET and Full Professor in the Biomedical Engineering program at UHIBA. Dr. Marcelo Risk holds a PhD from the School of Medicine of the Universidad de Buenos Aires, an ScD in Bioengineering from the Universidad Nacional de Córdoba, an MBA from the Universidad de Palermo, and a degree in Electronics Engineering from the Universidad Tecnologica Nacional, Argentina.

References

M. Malik, "Heart rate variability: standards of measurement, physiological interpretation and clinical use," Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, Circulation. 1996 Mar 1, vol. 93, no. 5, pp. 1043-65, PMID: 8598068.

Gallardo, G. Bellone, and M. Risk, "Heart rate variability: Validity of autonomic balance indicators in ultra-short recordings," in Applied Informatics: Fourth International Conference, ICAI 2021, Buenos Aires, Argentina, October 28–30, 2021, Proceedings 4, pp. 303-315, Springer International Publishing, doi: https://doi.org/10.1007/978-3-030-89654-6_22.

G. Lu, F. Yang, J. Taylor and J. Stein, "A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects," J. Med. Eng. Technol, vol. 33, no 8, pp. 634-641, 2009, doi:10.3109/03091900903150998.

J. Gallardo, G. Bellone and M. Risk, "Ultra-short heart rate variability and Poincaré plots," Paradigm Plus, vol. 2, no 3, pp. 37-52, 2021, doi:https://doi.org/10.55969/paradigmplus.v2n3a3.

F. Esgalhado, A. Batista, V. Vassilenko, S. Russo and M. Ortigueira, "Peak Detection and HRV Feature Evaluation on ECG and PPG Signals," Symmetry, vol. 14, no. 6, pp. 1139, March 2022, doi:https://doi.org/10.3390/sym14061139.

B. Johnston, R. Barrett-Jolley, A. Krige and I. Welters, "Heart rate variability: Measurement and emerging use in critical care medicine", J. Intensive Care Soc., vol. 21,no. 2, pp. 148–157, May. 2020, doi:10.1177/1751143719853744.

D. Bishop, R. Wise, C. Lee, R. Von Rahden and R. Rodseth, "Heart rate variability predicts 30-day all cause mortality in intensive care nits," South. African J. Anaesth. Analg., vol. 22, no. 4, pp. 125-128, Jul. 2016, doi: https://doi.org/10.1080/22201181.2016.1202605.

F. Hasty, G. García H. Dávila, S. Wittels, S. Hendricks and S. Chong,"Heart Rate Variability as a Possible Predictive Marker for Acute nflammatory Response in COVID-19 Patients", Mil. Med., vol. 186 no. 1-2, pp. e34–e38, January 2021, doi:https://doi.org/10.1093/milmed/saa405.

G. Quer, A. Alasaad and R. R. Rao, "On the Accuracy of Heart Rate Variability Measures from undersampled RR Interval Time Series," 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA, 2016, pp. 1-7, doi: 10.1109/GLOCOM.2016.7842045.

Bent B and Dunn JP. Optimizing sampling rate of wrist-worn optical sensors for physiologic monitoring. Journal of Clinical and Translational Science, page 1 of 8. doi: 10.1017/cts.2020.526.

M. Risk, J. Bruno, M. Soria, P. Arini and R. Taborda, "Measurement of QT interval and duration of the QRS complex at different ECG sampling rates," In Computers in Cardiology, pp. 495-498, September 2005, doi:10.1109/CIC.2005.1588146.

T. Ziemssen, J. Gasch and H. Ruediger, "Influence of ECG sampling frequency on spectral analysis of RR intervals and baroreflex sensitivity using the EUROBAVAR data set," Journal of Clinical Monitoring and Computing, 2008 vol. 22, pp. 159-168, doi:10.1007/10877-008-9117-0.

Y. Nishikawa, S. Izumi, Y. Yano, H. Kawaguchi and M. Yoshimoto, "Sampling Rate Reduction for wearable Heart Rate Variability Monitoring," Proc. IEEE Int. Symp. Circuits Syst., isbn = 9781538648810, issn = 02714310, May. 2018, doi: 10.1109/ISCAS.2018.8351558.

S. Béres and L. Hejjel, "The minimal sampling frequency of the photoplethysmogram for accurate pulse rate variability parameters in healthy volunteers," Biomed. Signal Process. Control, vol. 68, Jul. 2021, doi = 10.1016/j.bspc.2021.102589.

K. F. A. Lee, E. Chan, J. Car, W. Gan and G. Christopoulos, "Lowering the Sampling Rate: Heart Rate Response during Cognitive Fatigue," Biosensors 2022, vol. 12, pp. 315, doi:https://doi.org/10.3390/bios12050315.

A. Shintomi, Sh. Izumi, M. Yoshimoto and H. Kawaguchi, "Effectiveness of the heartbeat interval error and compensation method on heart," Healthc. Technol. Lett, vol.1-2, no. 9, pp. 9-15, 2022, issn = 20533713, doi: 10.1049/htl2.12023.

H. K. Chen, Y. F. Hu, S. F. Lin, "Methodological considerations in calculating heart rate variability based on wearable device heart rate samples", Computers in Biology and Medicine, 2018, doi:10.1016/j.compbiomed.2018.08.023.

J. Gallardo, G. Bellone, R. Acevedo and M. Risk, "Ultra-short-term heart rate variability analysis: comparison between Poincare and frequency domain methods," IEEE Latin America Transactions, vol. 20, no. 1, pp. 180-188, JANUARY 2021.

D. Singh, K. Vinod, and S. C. Saxena, "Sampling frequency of the RR interval time series for spectral analysis of heart rate variability," J. Med. Eng. Technol., vol. 28, no. 6, pp. 263–272, 2004, doi: 10.1080/03091900410001662350.

Y. Nishikawa, S. Izumi, Y. Yano, H. Kawaguchi and M. Yoshimoto, "Sampling Rate Reduction for Wearable Heart Rate Variability Monitoring," 2018 IEEE International Symposium on Circuits and Systems (ISCAS), Florence, Italy, 2018, pp. 1-5, doi: 10.1109/ISCAS.2018.8351558.

A. Tobola et al., "Sampling rate impact on energy consumption of biomedical signal processing systems," 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA, USA, 2015, pp. 1-6, doi: 10.1109/BSN.2015.7299392.

Ch. Hung-Kai, H. Yu-Feng, L. Shien-Fong. "Methodological considerations in calculating heart rate variability based on wearable device heart rate samples," Computers in Biology and Medicine, vol. 102, no. 11, pp. 396-401, 2018, https://doi.org/10.1016/j.compbiomed.2018.08.023.

J. F Sobh, M. Risk, R. Barbier and J Philip Saul. "Database for ECG, arterial blood pressure, and respiration signal analysis: feature extraction, spectral estimation, and parameter quantification," in Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society," pp. 955–956, 20-23 September, Montreal, Canada, 1995. ISBN:0-7803-2475-7, doi: 10.1109/IEMBS.1995.579378.

I. Bonyhay, M. Risk and R.Freeman, "High-pass filter characteristics of the baroreflex–a comparison of frequency domain and pharmaco logical methods," PLoS One, vol. 8,no. 11, pp. e79513, 2013, doi:https://doi.org/10.1371/journal.pone.0079513.

J McNames, T Thong, and M Aboy, "Impulse rejection filter for artifact removal in spectral analysis of biomedical signals," Proc. 26th Annu. Int. Conf. IEEE EMBS, San Fr. CA, USA, vol. 1, pp. 145–8, 2004, doi:10.1109/IEMBS.2004.1403112.

R. A. Thuraisingham, "Preprocessing RR interval time series for heart rate variability analysis and estimates of standard deviation of RR ntervals," Comput. Methods Programs Biomed., vol. 83, no. 1, pp. 78–82, 2006, doi: 10.1016/j.cmpb.2006.05.002.

A. E. Aubert, D. Ramaekers and F. Beckers, "Analysis of heart rate variability in unrestrained rats. Assessment of method and results," Med. Biol. Eng. Comp., vol. 60, pp. 197–213, 1999, doi: 10.1016/s0169-2607(99)00017-6.

D. Wejer, D. Makowiec, Z.R. et. al. Struzik, and M. ˙Zarczy´nska Buchowiecka. Impact of the Editing of Patterns with Abnormal RR intervals on the Assessment of Heart Rate Variability. Acta Phys. Pol. B, vol. 45, no. 11, pp. 2103, 2014, doi: 10.5506/APhysPolB.45.2103.

J. Gallardo, G. Bellone, S. Plano, D. Vigo and Marcelo Risk, "Heart Rate Variability: Influence of Pre-processing Methods in Identifying Single-Night Sleep-Deprived Subjects," J. Med. Biol. Eng., vol. 41, pp. 224-230, January 2021, doi: https://doi.org/10.1007/s40846-020-00595-8.

S. N. Karmali, A. Sciusco, S. M. May, and G. L. Ackland, "Heart rate variability in critical care medicine: a systematic review," Intensive Care Med. Exp., vol. 5, no. 1, pp. 1–15, 2017, doi: 10.1186/s40635-017-0146-1.

G. B. Moody, "Spectral analysis of heart rate without resampling," Proceedings of Computers in Cardiology Conference, London, UK, 1993, pp. 715-718, doi: 10.1109/CIC.1993.378302.

A. Delane, J. Bohórquez, S. Gupta and M. Schiavenato, "Lomb algorithm versus fast fourier transform in heart rate variability analyses of pain in premature infants," 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 2016, pp. 944-947, doi: 10.1109/EMBC.2016.7590857.

J. M. Bland and D. G. Altman, "Dynamics of Polymeric Liquids, Volume 2: Kinetic Theory," 2nd Edition, SBN: 978-0-471-80244-0, pp. 464, June 1987.

J.M.Bland and D.G.Altman, "Statistical methods for assessing agreement between two methods of clinical measurement," Lancet, vol. 1, no. 8476, pp. 307–310, 1986, doi: 10.1016/j.ijnurstu.2009.10.001.

M. Malik, "Heart Rate Variability", Futura Publishing Company Inc., New York, 1 edition, 1995, ISBN-10: 087993607X.

M. Malik and A. J. Camm, "Dynamic electrocardiography," Futura edition, 2007, doi:10.1002/9780470987483.

Chuangchai W, Pothisiri W. Postural Changes on Heart Rate Variability among Older Population: A Preliminary Study. Curr Gerontol Geriatr Res. 2021 Feb 27;2021:6611479. doi: 10.1155/2021/6611479. PMID:

; PMCID: PMC7937484.

F. Shaffer and J. P. Ginsberg, “An Overview of Heart Rate Variability Metrics and Norms,” Frontiers in Public Health, vol. 5, pp. 1-17, September 2017, doi: 10.3389/fpubh.2017.00258.

R. A. Hoshi, C. M. Pastre, L. C. Marques Vanderlei, and M. Fernandes Godoy, "Poincaré plot indexes of heart rate variability: Relationships with other nonlinear variables," Auton. Neurosci. Basic Clin., vol. 177,

no. 2, pp. 271–274, 2013, doi: 10.1016/j.autneu.2013.05.004.

P. Guzik, J. Piskorski, T. Krauze, R. Schneider, K. H. Wesseling, A. Wykretowicz, and H. Wysocki, "Correlations between the Poincaré Plot and Conventional Heart Rate Variability Parameters Assessed during Paced Breathing", J. Physiol. Sci., vol. 57, no. 1, pp. 63–71, 2007, doi:10.2170/physiolsci.RP005506

Downloads

Published

2025-01-30

How to Cite

San Roman, E., Zelechower, J., Gallardo, J. M., & Risk, M. (2025). Agreement analysis of heart rate variability indices at two different sampling rates for monitoring applications. IEEE Latin America Transactions, 23(3), 258–264. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/9270

Issue

Vol. 23 No. 3 (2025): Ordinary Issue

Section

Electronics