Computers in Biology and Medicine
Volume 42, Issue 2 , Pages 164-170 , February 2012

Spectral analysis of heart rate variability with the autoregressive method: What model order to choose?

  • Eduardo Miranda Dantas

      Affiliations

    • Department of Physiological Sciences, Federal University of Espírito Santo, Av. Marechal Campos, 1468 Vitoria, Espírito Santo, Brazil
  • ,
  • Marcela Lima Sant'Anna

      Affiliations

    • Department of Physiological Sciences, Federal University of Espírito Santo, Av. Marechal Campos, 1468 Vitoria, Espírito Santo, Brazil
  • ,
  • Rodrigo Varejão Andreão

      Affiliations

    • Federal Institute of Espírito Santo, Av. Vitória, 1728 Vitória, Espírito Santo, Brazil
    • Corresponding Author InformationCorresponding author.
  • ,
  • Christine Pereira Gonçalves

      Affiliations

    • Department of Physiological Sciences, Federal University of Espírito Santo, Av. Marechal Campos, 1468 Vitoria, Espírito Santo, Brazil
  • ,
  • Elis Aguiar Morra

      Affiliations

    • Department of Physiological Sciences, Federal University of Espírito Santo, Av. Marechal Campos, 1468 Vitoria, Espírito Santo, Brazil
  • ,
  • Marcelo Perim Baldo

      Affiliations

    • Department of Physiological Sciences, Federal University of Espírito Santo, Av. Marechal Campos, 1468 Vitoria, Espírito Santo, Brazil
  • ,
  • Sérgio Lamêgo Rodrigues

      Affiliations

    • Department of Physiological Sciences, Federal University of Espírito Santo, Av. Marechal Campos, 1468 Vitoria, Espírito Santo, Brazil
  • ,
  • José Geraldo Mill

      Affiliations

    • Department of Physiological Sciences, Federal University of Espírito Santo, Av. Marechal Campos, 1468 Vitoria, Espírito Santo, Brazil

Received 12 May 2011 ,Accepted 10 November 2011.

References 

  1. Akselrod S, Gordon D, Ubel FA, Shannon DC, Berger AC, Cohen RJ. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science. 1981;213:220–222
  2. Fallen EL, Kamath MV, Ghista DN. Power spectrum of heart rate variability: a non-invasive test of integrated neurocardiac function. Clin. Invest. Med. 1988;11:331–340
  3. Pomeranz B, Macaulay RJ, Caudill MA, Kutz I, Adam D, Gordon D, et al. Assessment of autonomic function in humans by heart rate spectral analysis. Am. J. Physiol. 1985;248:151–153
  4. Stein PK, Rich MW, Rottman JN, Kleiger RE. Stability of index of heart rate variability in patients with congestive heart failure. Am. Heart. J. 1995;129:975–981
  5. Burger AJ, Charlamb M, Weinrauch LA, D'Elia. JA. Short- and long-term reproducibility of heart rate variability in patients with long-standing type I diabetes mellitus. Am J Cardiol. 1997;80:1198–1202
  6. Nolan J, Flapan AD, Goodfield NE, Prescott RJ, Bloomfield P, Neilson JM. Measurement of parasympathetic activity from 24-hour ambulatory electrocardiograms and its reproducibility and sensitivity in normal subjects, patients with symptomatic myocardial ischemia, and patients with diabetes mellitus. Am. J. Cardiol. 1996;77:154–158
  7. Ewing DJ, Neilson JM, Shapiro CM, Stewart JA, Reid W. Twenty four hour heart rate variability: effects of posture, sleep, and time of day in healthy controls and comparison with bedside tests of autonomic function in diabetic patients. Br. Heart J. 1991;65:239–244
  8. Malpas SC, Maling TJ. Heart-rate variability and cardiac autonomic function in diabetes. Diabetes. 1990;39:1177–1181
  9. Konrady AO, Rudomanov OG, Yacovleva OI, Shlyakhto EV. Power spectral components of heart rate variability in different types of cardiac remodeling in hypertensive patients. Med. Sci. Monit. 2001;7:58–63
  10. Stein PK, Kleiger RE. Insights from the study of heart rate variability. Annu. Rev. Med. 1999;50:249–261
  11. Scalvini S, Volterrani M, Zanelli E, Pagani M, Mazzuero G, Coats AJ. Is heart rate variability a reliable method to assess autonomic modulation in left ventricular dysfunction and heart failure? Assessment of autonomic modulation with heart rate variability. Int. J. Cardiol. 1998;67:9–17
  12. Bigger JT, Fleiss JL, Steinman RC, Rolnitzky LM, Schneider WJ, Stein. PK. RR variability in healthy, middle-aged persons compared with patients with chronic coronary heart disease or recent acute myocardial infarction. Circulation. 1995;91:1936–1943
  13. Weber F, Schneider H, Von Arnim T, Urbaszek W. Heart rate variability and ischaemia in patients with coronary heart disease and stable angina pectoris; influence of drug therapy and prognostic value. TIBBS investigators group. Total ischemic burden bisoprolol Study. Eur. Heart J. 1999;20:38–50
  14. Van Boven AJ, Jukema JW, Haaksma J, Zwinderman AH, Crijns HJ, Lie KI. Depressed heart rate variability is associated with events in patients with stable coronary artery disease and preserved left ventricular function. Am. Heart J. 1998;135:571–576
  15. Nolan J, Batin PD, Andrews R, Lindsay SJ, Brooksby P, Mullen M. Prospective study of heart rate variability and mortality in chronic heart failure: results of the United Kingdom heart failure evaluation and assessment of risk trial (UK-heart). Circulation. 1998;98:1510–1516
  16. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology . Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur. Heart J. 1996;17:354–381
  17. Malliani A, Pagani M, Lombardi F, Cerutti S. Cardiovascular neural regulation explored in the frequency domain. Circulation. 1991;84:482–492
  18. L. Marple. Resolution of conventional Fourier, autoregressive and special ARMA methods of spectral analysis, in: Proceedings of IEEE International Conference on ASSP 1977, pp. 74–77.
  19. Kay SM, Marple SL. Spectrum analysis—a modern perspective. Proc. IEEE. 1981;69:1380–1419
  20. Boardman A, Schlindwein FS, Rocha AP, Leite. A. A study on the optimum order of autoregressive models for heart rate variability. Physiol. Meas. 2002;23:325–336
  21. Akaike. H. A new look at the statistical model identification. IEEE Trans. Autom. Control. 1974;19:716–723
  22. E. Parzen. Multiple Time Series: Determining the Order of Approximating Autoregressive Schemes, Technical report no. 23, Statistical Sciences Division, State University of New York, Buffalo, NY, 1975.
  23. Rissanen J. Universal coding, information prediction and estimation. IEEE Trans. Inf. Theory. 1984;30:629–636
  24. Tarvainen MP, Georgiadis SD, Ranta-aho PO, Karjalainen PA. Time-varying analysis of heart rate variability signals with Kalman smoother algorithm. Physiol. Meas. 2006;27:225–239
  25. Takalo R, Hytti H, Ihalainen H. Tutorial on univariate autoregressive spectral analysis. J. Clin. Monit. Comput. 2005;19:401–410
  26. Mendez MO, Bianchi AM, Villiantieri OP, Cerutti S, Penzel. T. Time-variant spectral analysis of the heart rate variability during sleep in healthy and obstructive sleep apnoea subjects. Comput. Cardiol. 2006;33:741–744
  27. Malik M. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Eur. Heart J. 1996;17:354–381
  28. Carvalho JLA, Rocha AF, Santos I, Itiki C, Junqueira LF, Nascimento FAO. Study on the optimal order for the auto-regressive time–frequency analysis of heart rate variability. Proc. Eng. Med. Bio. Comp. 2003;3:2621–2624
  29. Shrout PE, Fleiss Intraclass JL. Correlations: uses in assessing rater reliability. Psychol. Bull. 1979;2:420–428
  30. Schlogl A, Lee F, Bischof H, Pfurtscheller G. Characterization of four-class motor imagery EEG data for the BCI-competition. J. Neural Eng. 2005;2:14–22
  31. Ince NF, Arica S, Tewfik A. Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time-frequency tailings. J. Neural. Eng. 2006;3:235–244

PII: S0010-4825(11)00223-X

doi: 10.1016/j.compbiomed.2011.11.004

Computers in Biology and Medicine
Volume 42, Issue 2 , Pages 164-170 , February 2012