[go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Heart rate variability indices for very short-term (30 beat) analysis. Part 1: survey and toolbox

  • Original Research
  • Published:
Journal of Clinical Monitoring and Computing Aims and scope Submit manuscript

Abstract

Heart rate variability (HRV) analysis over very short (<60 s) periods may be useful for monitoring dynamic changes in autonomic nervous system activity where steady-state conditions are not maintained (e.g. during drug administration, or the start or end of exercise). From the 1980s there has been a wealth of HRV indices produced in the quest for better measures of the change in parasympathetic and sympathetic activity. Many of the indices have been sparingly used and have not been investigated for application to short-term use. This study surveyed published methods of HRV analysis searching for indices that could be applied to very short time HRV analysis. The survey included measures of time domain, frequency domain, respiratory sinus arrhythmia, Poincaré plot, and heart rate characteristics. Indices were tested with short segments of archived data to remove those that produced invalid results, or were mathematically equivalent to, but less well known than other indices. The survey identified a comprehensive list of 115 indices that were subsequently coded and screened. Of these, 70 were unique and produced a finite number with 60 s data, so are included in the Toolbox. These indices require validation against physiological data before they can be applied to short-term HRV analysis of cardiac autonomic nervous system activity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Goldberger JJ, Le FK, Lahiri M, Kannankeril PJ, Ng J, Kadish AH. Assessment of parasympathetic reactivation after exercise. Am J Physiol Heart Circ Physiol. 2006;290(6):H2446–52.

    Article  PubMed  CAS  Google Scholar 

  2. Vettorello M, Colombo R, De Grandis CE, Costantini E, Raimondi F. Effect of fentanyl on heart rate variability during spontaneous and paced breathing in healthy volunteers. Acta Anaesthesiol Scand. 2008;52(8):1064–70. doi:10.1111/j.1399-6576.2008.01713.x.

    Article  PubMed  CAS  Google Scholar 

  3. Schachinger H, Muller BU, Strobel W, Drewe J, Ritz R. Effect of midazolam on transfer function between beat-to-beat arterial pressure and inter-beat interval length. Br J Anaesth. 2000;84(3):316–22.

    Article  PubMed  CAS  Google Scholar 

  4. Alcalay M, Izraeli S, Wallach-Kapon R, Tochner Z, Benjamini Y, Akselrod S. Paradoxical pharmacodynamic effect of atropine on parasympathetic control: a study by spectral analysis of heart rate fluctuations. Clin Pharmacol Ther. 1992;52(5):518–27.

    Article  PubMed  CAS  Google Scholar 

  5. Task Force of European Society of Cardiology. 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. Eur Heart J. 1996;17(3):354–81.

    Article  Google Scholar 

  6. Raetz SL, Richard CA, Garfinkel A, Harper RM. Dynamic characteristics of cardiac R–R intervals during sleep and waking states. Sleep. 1991;14(6):526–33.

    PubMed  CAS  Google Scholar 

  7. Kamen PW, Tonkin AM. Application of the Poincaré plot to heart rate variability: a new measure of functional status in heart failure. Aust N Z J Med. 1995;25(1):18–26.

    Article  PubMed  CAS  Google Scholar 

  8. Toichi M, Sugiura T, Murai T, Sengoku A. A new method of assessing cardiac autonomic function and its comparison with spectral analysis and coefficient of variation of R–R interval. J Auton Nerv Syst. 1997;62(1–2):79–84.

    Article  PubMed  CAS  Google Scholar 

  9. Otzenberger H, Gronfier C, Simon C, Charloux A, Ehrhart J, Piquard F, Brandenberger G. Dynamic heart rate variability: a tool for exploring sympathovagal balance continuously during sleep in men. Am J Physiol. 1998;275(3 Pt 2):H946–50.

    PubMed  CAS  Google Scholar 

  10. Bergfeldt L, Haga Y. Power spectral and Poincaré plot characteristics in sinus node dysfunction. J Appl Physiol. 2003;94(6):2217–24.

    PubMed  Google Scholar 

  11. Boardman A, Schlindwein FS, Thakor NV, Kimura T, Geocadin RG. Detection of asphyxia using heart rate variability. Med Biol Eng Comput. 2002;40(6):618–24.

    Article  PubMed  CAS  Google Scholar 

  12. Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):E215–20.

    Article  PubMed  CAS  Google Scholar 

  13. Tulppo MP, Makikallio TH, Takala TE, Seppanen T, Huikuri HV. Quantitative beat-to-beat analysis of heart rate dynamics during exercise. Am J Physiol. 1996;271(1 Pt 2):H244–52.

    PubMed  CAS  Google Scholar 

  14. Smith AL, Reynolds KJ, Owen H. Correlated Poincaré indices for measuring heart rate variability. Australas Phys Eng Sci Med. 2007;30(4):336–41.

    PubMed  Google Scholar 

  15. Voss A, Schulz S, Schroeder R, Baumert M, Caminal P. Methods derived from nonlinear dynamics for analysing heart rate variability. Philos Transact A Math Phys Eng Sci. 2009;367(1887):277–96. doi:10.1098/rsta.2008.0232.

    Article  Google Scholar 

  16. Katona PG, Jih F. Respiratory sinus arrhythmia: noninvasive measure of parasympathetic cardiac control. J Appl Physiol. 1975;39(5):801–5.

    PubMed  CAS  Google Scholar 

  17. Seals DR, Chase PB. Influence of physical training on heart rate variability and baroreflex circulatory control. J Appl Physiol. 1989;66(4):1886–95.

    Article  PubMed  CAS  Google Scholar 

  18. Porges SW. Method and apparatus for evaluating rhythmic oscillations in aperiodic physiological response systems. United States Patent 4510944. 1985.

  19. Cohen ME, Hudson DL, Deedwania PC. Applying continuous chaotic modeling to cardiac signal analysis. IEEE Eng Med Biol Mag. 1996;15(5):97–102.

    Article  Google Scholar 

  20. Schmidt G, Malik M, Barthel P, Schneider R, Ulm K, Rolnitzky L, Camm AJ, Bigger JT Jr, Schomig A. Heart-rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction. Lancet. 1999;353(9162):1390–6.

    Article  PubMed  CAS  Google Scholar 

  21. Watanabe MA, Alford M, Schneider R, Bauer A, Barthel P, Stein PK, Schmidt G. Demonstration of circadian rhythm in heart rate turbulence using novel application of correlator functions. Heart Rhythm. 2007;4(3):292–300. doi:10.1016/j.hrthm.2006.11.016.

    Article  PubMed  Google Scholar 

  22. Brennan M, Palaniswami M, Kamen P. Do existing measures of poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Trans Biomed Eng. 2001;48(11):1342–7.

    Article  PubMed  CAS  Google Scholar 

  23. Arif M, Aziz W. Application of threshold-based acceleration change index (TACI) in heart rate variability analysis. Physiol Meas. 2005;26(5):653–65.

    Article  PubMed  CAS  Google Scholar 

  24. Laguna P, Moody GB, Mark RG. Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals. IEEE Trans Biomed Eng. 1998;45(6):698–715.

    Article  PubMed  CAS  Google Scholar 

  25. Moody GB. Spectral analysis of heart rate without resampling. Paper presented at the Computers in Cardiology. Proc., London, UK, 1993. p. 5–8.

  26. Chang KL, Monahan KJ, Griffin MP, Lake D, Moorman JR. Comparison and clinical application of frequency domain methods in analysis of neonatal heart rate time series. Ann Biomed Eng. 2001;29(9):764–74.

    Article  PubMed  CAS  Google Scholar 

  27. Clifford GD. Signal processing methods for heart rate variability. PhD Thesis, Oxford: Oxford University; 2002.

  28. Savransky D. Lomb (Lomb-Scargle) periodogram. The MathWorks. http://www.mathworks.com.au/matlabcentral/fileexchange/20004-lomb-lomb-scargle-periodogram (2008). Accessed 26 Aug. 2010.

  29. Press WH, Rybicki GB. Fast algorithm for spectral analysis of unevenly sampled data. Astrophys J. 1989;338:277–80.

    Article  Google Scholar 

  30. Hamilton RM, McKechnie PS, Macfarlane PW. Can cardiac vagal tone be estimated from the 10-second ECG? Int J Cardiol. 2004;95(1):109–15.

    Article  PubMed  Google Scholar 

  31. Thong T, Li K, McNames J, Aboy M, Goldstein B. Accuracy of ultra-short heart rate variability measures. Paper presented at the 25th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society. Cancun, Mexico: 2003. p 17–21.

  32. Fouad FM, Tarazi RC, Ferrario CM, Fighaly S, Alicandri C. Assessment of parasympathetic control of heart rate by a noninvasive method. Am J Physiol. 1984;246(6 Pt 2):H838–42.

    PubMed  CAS  Google Scholar 

  33. Sosnowski M, Petelenz T, Leski J. Return maps: a non-linear method for evaluation of respiratory sinus arrhythmia. Paper presented at the Computers in Cardiology 1994. Bethesda, MD: 1994 p. 25–28.

  34. Gaitan-Gonzalez MJ, Carrasco-Sosa S, Gonzalez-Camarena R, Yanez-Suarez O (2005) Non-linear relationship between heart period and root mean square of successive differences during ramp exercise and early recovery. Paper presented at the Computers in Cardiology 2005, Lyon, Sep. 25–28.

  35. Desok K, Yunhwan S, Sook-hyun K, Suntae J.Short term analysis of long term patterns of heart rate variability in subjects under mental stress. Paper presented at the Int. Conf. BioMedical Engineering and Informatics (BMEI 2008). Saanya, China; 2008. p. 27–30.

  36. Julu PO-O, Little CJ. Apparatus and method for measuring cardiac vagal tone. United States Patent 6442420. 2002.

  37. Little CJ, Julu PO, Hansen S, Reid SW. Real-time measurement of cardiac vagal tone in conscious dogs. Am J Physiol. 1999;276(2 Pt 2):H758–65.

    PubMed  CAS  Google Scholar 

  38. van Bemmel T, Vinkers DJ, Macfarlane PW, Gussekloo J, Westendorp RG. Markers of autonomic tone on a standard ECG are predictive of mortality in old age. Int J Cardiol. 2006;107(1):36–41.

    Article  PubMed  Google Scholar 

  39. Halliwill JR, Billman GE. Effect of general anesthesia on cardiac vagal tone. Am J Physiol. 1992;262(6 Pt 2):H1719–24.

    PubMed  CAS  Google Scholar 

  40. Porges SW. Vagal mediation of respiratory sinus arrhythmia. Implications for drug delivery. Ann N Y Acad Sci. 1991;618(1):57–66.

    Article  PubMed  CAS  Google Scholar 

  41. Kristiansen NK, Fleischer J, Jensen MS, Andersen KS, Nygaard H. Design and evaluation of a handheld impedance plethysmograph for measuring heart rate variability. Med Biol Eng Comput. 2005;43(4):516–21.

    Article  PubMed  CAS  Google Scholar 

  42. Ejskjaer N, Fleischer J, Jacobsen PE, Poulsen PL, Nygaard H. A pocket-size device to detect autonomic neuropathy. J Diabetes Sci Technol. 2008;2(4):692–6.

    PubMed  Google Scholar 

  43. Sandercock GR, Brodie DA. The use of heart rate variability measures to assess autonomic control during exercise. Scand J Med Sci Sports. 2006;16(5):302–13. doi:10.1111/j.1600-0838.2006.00556.x.

    Article  PubMed  CAS  Google Scholar 

  44. Malliani A, Pagani M, Montano N, Mela GS. Sympathovagal balance: a reappraisal [Letter]. Circulation. 1998;98(23):2640–3.

    Article  PubMed  CAS  Google Scholar 

  45. Salahuddin L, Cho J, Jeong MG, Kim D. Ultra short term analysis of heart rate variability for monitoring mental stress in mobile settings. Paper presented at the 29th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society. Lyon; 2007. p. 23–26.

  46. Salahuddin L, Jeong MG, Kim D Ultra short term analysis of heart rate variability using normal sinus rhythm and atrial fibrillation EGG data. Paper presented at the 9th Int. Conf. e-Health Networking, Application and Services. Taipei; 2007. 19–22.

  47. Balocchi R, Cantini F, Varanini M, Raimondi G, Legramante JM, Macerata A. Revisiting the potential of time-domain indexes in short-term HRV analysis. Biomed Tech (Berl). 2006;51(4):190–3.

    Article  Google Scholar 

  48. Goldberger JJ. Sympathovagal balance: how should we measure it? Am J Physiol. 1999;276(4 Pt 2):H1273–80.

    PubMed  CAS  Google Scholar 

  49. Griffin MP, Moorman JR. Toward the early diagnosis of neonatal sepsis and sepsis-like illness using novel heart rate analysis. Pediatrics. 2001;107(1):97–104.

    Article  PubMed  CAS  Google Scholar 

  50. Schirdewan A, Meyerfeldt U, Wessel N, Bondke HJ, Schreiber P, Sadowski R, Kamke W, Wiedemann M. Heart rate dynamics before the onset of ventricular tachyarrhythmias: Results of the cardioverter defibrillator registry MARITA. J Am College of Cardiol. 2004;43 (5, Supplement 1): A125–A126.

    Google Scholar 

  51. Wessel N, Malberg H, Bauernschmitt R, Kurths J. Nonlinear methods of cardiovascular physics and their clinical applicability. Int J Bifurc Chaos. 2007;17(10):3325–71. doi:10.1142/S0218127407019093.

    Article  Google Scholar 

  52. Voss A, Kurths J, Kleiner HJ, Witt A, Wessel N, Saparin P, Osterziel KJ, Schurath R, Dietz R. The application of methods of non-linear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death. Cardiovasc Res. 1996;31(3):419–33.

    PubMed  CAS  Google Scholar 

  53. Persson A, Solders G. R–R variations, a test of autonomic dysfunction. Acta Neurol Scand. 1983;67(5):285–93.

    Article  PubMed  CAS  Google Scholar 

  54. Persson A, Solders G. R–R variations in Guillain-Barré syndrome: a test of autonomic dysfunction. Acta Neurol Scand. 1983;67(5):294–300.

    Article  PubMed  CAS  Google Scholar 

  55. Bergfeldt BL, Edhag KO, Solders G, Vallin HO. Analysis of sinus cycle variation: a new method for evaluation of suspected sinus node dysfunction. Am Heart J. 1987;114(2):321–7.

    Article  PubMed  CAS  Google Scholar 

  56. Ribeiro AL, Cassini P, Peixoto SV, Lima-Costa MF. Vagal impairment in elderly Chagas disease patients: a population-based study (The Bambuí Study). Int J Cardiol. 2011;147(3):359–65. doi:10.1016/j.ijcard.2009.10.002.

    Article  PubMed  Google Scholar 

  57. Macfarlane PW, Norrie J. The value of the electrocardiogram in risk assessment in primary prevention: experience from the West of Scotland Coronary Prevention Study. J Electrocardiol. 2007;40(1):101–9. doi:10.1016/j.jelectrocard.2006.05.003.

    Article  PubMed  CAS  Google Scholar 

  58. de Bruyne MC, Kors JA, Hoes AW, Klootwijk P, Dekker JM, Hofman A, van Bemmel JH, Grobbee DE. Both decreased and increased heart rate variability on the standard 10-second electrocardiogram predict cardiac mortality in the elderly: the Rotterdam Study. Am J Epidemiol. 1999;150(12):1282–8.

    Article  PubMed  Google Scholar 

  59. Thakre TP, Smith ML. Loss of lag-response curvilinearity of indices of heart rate variability in congestive heart failure. BMC Cardiovasc Disord. 2006;6(1):27. doi:10.1186/1471-2261-6-27.

    Article  PubMed  Google Scholar 

  60. Tateno K, Glass L. Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and deltaRR intervals. Med Biol Eng Comput. 2001;39(6):664–71.

    Article  PubMed  CAS  Google Scholar 

  61. Copie X, Le Heuzey JY, Iliou MC, Khouri R, Lavergne T, Pousset F, Guize L. Correlation between time-domain measures of heart rate variability and scatterplots in postinfarction patients. Pacing Clin Electrophysiol. 1996;19(3):342–7.

    Article  PubMed  CAS  Google Scholar 

  62. Voss A, Hnatkova K, Wessel N, Kurths J, Sander A, Schirdewan A, Camm AJ, Malik M. Multiparametric analysis of heart rate variability used for risk stratification among survivors of acute myocardial infarction. Pacing Clin Electrophysiol. 1998;21(1 Pt 2):186–92.

    Article  PubMed  CAS  Google Scholar 

  63. Wessel N, Malberg H, Bauernschmitt R, Schirdewan A, Kurths J. Nonlinear additive autoregressive model-based analysis of short-term heart rate variability. Med Biol Eng Comput. 2006;44(4):321–30. doi:10.1007/s11517-006-0038-0.

    Article  PubMed  Google Scholar 

  64. Hyndman BW, Zeelenberg C (1993) Spectral analysis of heart rate variability revisited: comparison of the methods. Paper presented at the Computers in Cardiology. Proc., London; 1993. p. 5–8.

  65. Carrasco S, Gaitán MJ, González R, Yánez O. Correlation among Poincaré plot indexes and time and frequency domain measures of heart rate variability. J Med Eng Technol. 2001;25(6):240–8.

    Article  PubMed  CAS  Google Scholar 

  66. Smith AL, Reynolds KJ, Owen H. Heart Rate Variability Indices for Very Short-term (30 beat) Analysis. Part 2: Validation. J Clin Monit Comput Submitted for publication. 2013.

  67. Antelmi I, de Paula RS, Shinzato AR, Peres CA, Mansur AJ, Grupi CJ. Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease. Am J Cardiol. 2004;93(3):381–5.

    Article  PubMed  Google Scholar 

  68. Mietus JE, Peng CK, Henry I, Goldsmith RL, Goldberger AL. The pNNx files: re-examining a widely used heart rate variability measure. Heart. 2002;88(4):378–80.

    Article  PubMed  CAS  Google Scholar 

  69. Ewing DJ, Neilson JM, Travis P. New method for assessing cardiac parasympathetic activity using 24 hour electrocardiograms. Br Heart J. 1984;52(4):396–402.

    Article  PubMed  CAS  Google Scholar 

  70. Eckoldt K. Procedure and results of the quantitative automatic analysis of the heart frequency and their spontaneous variability. Dtsch Gesundheitswes. 1984;39:856–63.

    Google Scholar 

  71. Moser M, Lehofer M, Sedminek A, Lux M, Zapotoczky HG, Kenner T, Noordergraaf A. Heart rate variability as a prognostic tool in cardiology. A contribution to the problem from a theoretical point of view. Circulation. 1994;90(2):1078–82.

    Article  PubMed  CAS  Google Scholar 

  72. Eckoldt K. Problems and results of the analysis of sinus rhythm. Psychiatrie, Neurologie und medizinische Psychologie. 1990;43:53–63.

    CAS  Google Scholar 

  73. Guzik P, Piskorski J, Krauze T, Wykretowicz A, Wysocki H. Heart rate asymmetry by Poincaré plots of RR intervals. Biomed Tech (Berl). 2006;51(4):272–5.

    Article  Google Scholar 

  74. Piskorski J, Guzik P. Geometry of the Poincaré plot of RR intervals and its asymmetry in healthy adults. Physiol Meas. 2007;28(3):287–300.

    Article  PubMed  CAS  Google Scholar 

  75. Nikolopoulos S, Alexandridi A, Nikolakeas S, Manis G. Experimental analysis of heart rate variability of long-recording electrocardiograms in normal subjects and patients with coronary artery disease and normal left ventricular function. J Biomed Inform. 2003;36(3):202–17.

    Article  PubMed  CAS  Google Scholar 

  76. Kovatchev BP, Farhy LS, Cao H, Griffin MP, Lake DE, Moorman JR. Sample asymmetry analysis of heart rate characteristics with application to neonatal sepsis and systemic inflammatory response syndrome. Pediatr Res. 2003;54(6):892–8.

    Article  PubMed  Google Scholar 

  77. Contreras P, Canetti R, Migliaro ER. Correlations between frequency-domain HRV indices and lagged Poincaré plot width in healthy and diabetic subjects. Physiol Meas. 2007;28(1):85–94.

    Article  PubMed  Google Scholar 

  78. Hirose M, Imai H, Ohmori M, Matsumoto Y, Amaya F, Hosokawa T, Tanaka Y. Heart rate variability during chemical thoracic sympathectomy. Anesthesiology. 1998;89(3):666–70.

    Article  PubMed  CAS  Google Scholar 

  79. Van Hoogenhuyze D, Weinstein N, Martin GJ, Weiss JS, Schaad JW, Sahyouni XN, Fintel D, Remme WJ, Singer DH. Reproducibility and relation to mean heart rate of heart rate variability in normal subjects and in patients with congestive heart failure secondary to coronary artery disease. Am J Cardiol. 1991;68(17):1668–76.

    Article  PubMed  Google Scholar 

  80. Marciano F, Migaux ML, Acanfora D, Furgi G, Rengo F. Quantification of Poincaré maps for the evaluation of heart rate variability. Paper presented at the Computers in Cardiology. Bethesda; 1994. p. 25–28.

  81. Olesen RM, Thomsen PE, Saermark K, Glikson M, Feldman S, Lewkowicz M, Levitan J. Statistical analysis of the DIAMOND MI study by the multipole method. Physiol Meas. 2005;26(5):591–8.

    Article  PubMed  CAS  Google Scholar 

  82. Ashkenazy Y, Ivanov PC, Havlin S, Peng CK, Goldberger AL, Stanley HE. Magnitude and sign correlations in heartbeat fluctuations. Phys Rev Lett. 2001;86(9):1900–3.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Biomedical Engineering Dept., Flinders Medical Centre.

Author information

Authors and Affiliations

  1. The Medical Device Research Institute, Flinders University, GPO Box 2100, Adelaide, SA, 5001, Australia

    Anne-Louise Smith & Karen J. Reynolds

  2. Biomedical Engineering Dept, Flinders Medical Centre, Adelaide, Australia

    Anne-Louise Smith

  3. School of Medicine, Flinders University, GPO Box 2100, Adelaide, SA, 5001, Australia

    Harry Owen

Authors
  1. Anne-Louise Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harry Owen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karen J. Reynolds
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anne-Louise Smith.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 161 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Smith, AL., Owen, H. & Reynolds, K.J. Heart rate variability indices for very short-term (30 beat) analysis. Part 1: survey and toolbox. J Clin Monit Comput 27, 569–576 (2013). https://doi.org/10.1007/s10877-013-9471-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10877-013-9471-4

Keywords