Analysis of Heart Rate Variability

A Review
  • Otto Rompelman
  • Ben J. TenVoorde


Fluctuations in heart rate have long been the subject of investigation for almost as long as the electrocardiogram has been measured. Fluctuations with a period in the order of 2 to 100 seconds are usually referred to as heart rate variability (HRV) and are mainly of neuronal origin. This implies that the analysis of HRV may shed some light on the autonomous nervous system as it influences the neuro-cardiovascular system. Two main issues emerge if we want to analyze fluctuations in heart rate, viz. (a) how and with what accuracy can heart rate be assessed, and (b) in which way can variations in heart rate be quantified and analyzed? The lower bounds for the accuracy are discussed leading to the intrinsic signal-to-noise ratio of HRV. Consequently, the event series analysis of the cardiac event is reviewed and an interesting application of this approach is shown.


Heart Rate Heart Rate Variability Respiratory Sinus Arrhythmia Occurrence Time Event Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Akselrod. S., Gordon.. D.. libel. F. A., Shannon, D. C., Barger, A. C., and Cohen, R. J.. 1981, Power spectrum analysis of heart rate fluctuations: a quantitative probe of beat-to-beat cardiovascular control, Science 213: 220–223.CrossRefGoogle Scholar
  2. Bayly, E. J., 1968, Spectral analysis of pulse frequency modulation in the nervous system, IEEE Trans. Biomed. Eng. BME-15: 257–265.Google Scholar
  3. DeBoer, R. W., Karemaker, J. M., and Strackee, J., 1983, Beat-to-Beat variability of heart interval and blood pressure, Automedica 4: 217–222.Google Scholar
  4. Coenen, A. J. R. M., Rompelman, O., and Kitney, R. I., 1977, Measurement of heart rate variability: Part II — Hardware digital device for the assessment of heart rate variability, Med. & Biol. Eng. & Comp. 15: 423–430.CrossRefGoogle Scholar
  5. Einthoven, W., 1985, Über die Form des menschlichen Elektrocardiogramms, Pflügers Arch. ges. Physiol. 60: 101–123.Google Scholar
  6. Einthoven, W., 1903, Die galvanometrische Registrierung des menschlichen Elektrokardiogramms, zugleich eine Beurteilung der Anwendung des Capillar-Elektrometers in der Physiologic, Pflügers Arch. ges. Physiol. 99: 472–480.CrossRefGoogle Scholar
  7. Hyndman, B. W., 1970, A digital simulation of the human cardiovascular system and its use in the study of sinus arrhythmia, Ph.D. Thesis, University of London.Google Scholar
  8. Hyndman, B. W. and Mohn, R. K., 1973, A pulse modulator model of pacemaker activity, Digest of the 10-th Int. Conf. on Med. & Biol. Eng., Dresden, p. 223.Google Scholar
  9. Koeleman, A. S. M., Van den Akker, T. J., Ros, H. H., Janssen, R. T., Rompelman, 0., 1984, Estimation accuracy of P wave and QRS complex occurcnce times in the ECG: the accuracy for simplified theoretical and computer simulated waveforms, Signal Processing: 7: 389–405.Google Scholar
  10. Kitney, R. I., Rompelman, O. (eds.), 1980, The Study of Heart Rate Variability, Clarendon Press: Oxford (UK).Google Scholar
  11. Kitney, R. I., Rompelman. O. (eds.), 1987, The Beat-by-heat Investigation of Cardiovascular Function, Clarendon Press: Oxford (UK).Google Scholar
  12. Latson, T. W., 1994, Principles and applications of heart rate variability analysis. In: Lynch C. III (ed.), Clinical Cardiac Electrophysiology: Perioperative Considerations, Lippincott Company: Philadelphia, pp. 307–348.Google Scholar
  13. Ludwig, C., 1847, Beiträge zur Kenntnis des Einflusses der Respirationsbewegungen auf der Blutumlauf im Aortensystem, Arch. Anat. Physiol. Wissenschafil. Med. p. 242–257.Google Scholar
  14. Pagani, M., Lombardi, F., Guzetti, S., Rimoldi, O., Furlan, R., Pizzinelli, P.. Sandrone, G., Malfatto, G., Dell’Orto, S., Picculagu, E., Turiel, M., Baselli, G., Cerotti, S., Malliani, A.. 1986, Power spectral analysis of heart rate and arterial blood pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dogs, Circ. Res. 59: 178–193.Google Scholar
  15. Pehäz, J., 1978, Mayer waves: history and methodology, Automedica 2: 135–141.Google Scholar
  16. Rompelman, O., Coenen, A. J. R. M., Kitney, R. I., 1977, Measurement of heart rate variability: Part I -Comparative study of heart rate variability analysis methods, Med. & Biol. Eng. & Comp. 15: 233–239.CrossRefGoogle Scholar
  17. Rompelman, 0., 1986, Tutorial review on the analysis of cardiac event series: a signal analysis approach, Automedica 7: 191–212.Google Scholar
  18. Rompelman, O., Janssen, R. J., Koeleman, A. S. M., van den Akker, T. J., and Ros, H. H., 1986, Practical limitations for the estimation of P-wave and QRS-complex occurrence times, Automedica 6: 269–284.Google Scholar
  19. Saul, J. P., Berger, R. D. Chen, M. H., and Cohen, R. J., 1989, Transferfunction analysis of autonomic regulation II. Respiratory sinus arrhythmia, Am. J. Physiol. 256 (Heart Circ. Physiol. 25 ): H153 - H161.Google Scholar
  20. Sayers, B. McA., 1973, Analysis of heart rate variability, Ergonomics 16: 17–32.CrossRefGoogle Scholar
  21. TenVoorde, 1992, B. J., Modelling the Baroreflex, PhD-thesis. Free University, Amsterdam.Google Scholar

Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Otto Rompelman
    • 1
  • Ben J. TenVoorde
    • 2
  1. 1.Department of Electrical EngineeringDelft University of TechnologyDelftThe Netherlands
  2. 2.Medical Physics and Informatics Group Academic HospitalFree UniversityAmsterdamThe Netherlands

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