Medical & Biological Engineering & Computing

, Volume 49, Issue 1, pp 67–74 | Cite as

Discrimination power of long-term heart rate variability measures for chronic heart failure detection

  • Paolo Melillo
  • Roberta Fusco
  • Mario Sansone
  • Marcello Bracale
  • Leandro Pecchia
Original Article


The aim of this study was to investigate the discrimination power of standard long-term heart rate variability (HRV) measures for the diagnosis of chronic heart failure (CHF). The authors performed a retrospective analysis on four public Holter databases, analyzing the data of 72 normal subjects and 44 patients suffering from CHF. To assess the discrimination power of HRV measures, an exhaustive search of all possible combinations of HRV measures was adopted and classifiers based on Classification and Regression Tree (CART) method was developed, which is a non-parametric statistical technique. It was found that the best combination of features is: Total spectral power of all NN intervals up to 0.4 Hz (TOTPWR), square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD) and standard deviation of the averages of NN intervals in all 5-min segments of a 24-h recording (SDANN). The classifiers based on this combination achieved a specificity rate and a sensitivity rate of 100.00 and 89.74%, respectively. The results are comparable with other similar studies, but the method used is particularly valuable because it provides an easy to understand description of classification procedures, in terms of intelligible “if … then …” rules. Finally, the rules obtained by CART are consistent with previous clinical studies.


Heart rate variability (HRV) Chronic heart failure (CHF) Classification and regression tree (CART) Data-mining 


  1. 1.
    Arbolishvili GN, Mareev VY, Orlova YA, Belenkov YN (2006) Heart rate variability in chronic heart failure and its role in prognosis of the disease. Kardiologiya 46(19):4–11PubMedGoogle Scholar
  2. 2.
    Aronson D, Mittleman MA, Burger AJ (2004) Measures of heart period variability as predictors of mortality in hospitalized patients with decompensated congestive heart failure. Am J Cardiol 93(54):59–63CrossRefPubMedGoogle Scholar
  3. 3.
    Asyali MH (2003) Discrimination power of long-term heart rate variability measures. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Cancun, pp 200–203Google Scholar
  4. 4.
    Bigger JT, Fleiss JL, Steinman RC, Rolnitzky LM, Kleiger RE, Rottman JN (1992) Correlations among time and frequency-domain measures of heart period variability 2 weeks after acute myocardial-infarction. Am J Cardiol 69(5693):891–898CrossRefPubMedGoogle Scholar
  5. 5.
    Bigger JT, Fleiss JL, Steinman RC, Rolnitzky LM, Schneider WJ, Stein PK (1995) RR variability in healthy, middle-aged persons compared with patients with chronic coronary heart-disease or recent acute myocardial-infarction. Circulation 91(1095):1936–1943PubMedGoogle Scholar
  6. 6.
    Breiman L (1984) Classification and regression trees. Wadsworth International Group, BelmontGoogle Scholar
  7. 7.
    Cios KJ, Moore GW (2002) Uniqueness of medical data mining. Artif Intell Med 26(1282):1–24CrossRefPubMedGoogle Scholar
  8. 8.
    Esposito F, Malerba D, Semeraro G (1997) A comparative analysis of methods for pruning decision trees. IEEE Trans Pattern Anal 19(1304):476–491CrossRefGoogle Scholar
  9. 9.
    Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(557):215–220Google Scholar
  10. 10.
    Guzzetti S, Magatelli R, Borroni E, Mezzetti S (2001) Heart rate variability in chronic heart failure. Auton Neurosci Basic Clin 90(64):102–105CrossRefGoogle Scholar
  11. 11.
    Hadase M, Azuma A, Zen K, Asada S, Kawasaki T, Kamitani T, Kawasaki S, Sugihara H, Matsubara H (2004) Very low frequency power of heart rate variability is a powerful predictor of clinical prognosis in patients with congestive heart failure. Circ J 68(49):343–347CrossRefPubMedGoogle Scholar
  12. 12.
    Ihlen EAF (2009) A comparison of two hilbert spectral analyses of heart rate variability. Med Biol Eng Comput 47(6897):1035–1044CrossRefPubMedGoogle Scholar
  13. 13.
    Isler Y, Kuntalp M (2007) Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure. Comput Biol Med 37(15):1502–1510CrossRefPubMedGoogle Scholar
  14. 14.
    Jain AK, Duin RPW, Jianchang M (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(183):4–37CrossRefGoogle Scholar
  15. 15.
    Jessup M, Abraham WT, Casey DE, Feldman AM, Francis GS, Ganiats TG, Konstam MA, Mancini DM, Rahko PS, Silver MA, Stevenson LW, Yancy CW, Hunt SA, Chin MH, Jessup M, Michl K, Oates JA, Smith SC, Jacobs AK, Buller CE, Creager MA, Ettinger SM, Krumholz HM, Kushner FG, Lytle BW, Nishimura RA, Page RL, Tarkington LG, Yancy CW, Lewin JC, May C, Bradfield L, Stewart MD, Keller S, McDougall A, Brown N, Whitman GR (2009) 2009 focused update: ACCF/AHA guidelines for the diagnosis and management of heart failure in adults a report of the american college of cardiology foundation/american heart association task force on practice guidelines developed in collaboration with the international society for heart and lung transplantation. J Am Coll Cardiol 53(145):1343–1382CrossRefGoogle Scholar
  16. 16.
    Khoshgoftaar TM, Allen EB (2001) Controlling overfitting in classification-tree models of software quality. Empir Softw Eng 6(5698):59–79CrossRefGoogle Scholar
  17. 17.
    Kruger C, Lahm T, Zugck C, Kell R, Schellberg D, Schweizer MWF, Kubler W, Haass A (2002) Heart rate variability enhances the prognostic value of established parameters in patients with congestive heart failure. Z Kardiol 91(58):1003–1012PubMedGoogle Scholar
  18. 18.
    Krzanowski WJ (1977) The performance of fisher’s linear discriminant function under non-optimal conditions. Technometrics 19(5744):191–200CrossRefGoogle Scholar
  19. 19.
    La Rovere MT, Pinna GD, Maestri R, Mortara A, Capomolla S, Febo O, Ferrari R, Franchini M, Gnemmi M, Opasich C, Riccardi PG, Traversi E, Cobelli F (2003) Short-term heart rate variability strongly predicts sudden cardiac death in chronic heart failure patients. Circulation 107(57):565–570CrossRefPubMedGoogle Scholar
  20. 20.
    Laguna P, Moody GB, Mark RG (1998) Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals. IEEE Trans Biomed Eng 45(559):698–715CrossRefPubMedGoogle Scholar
  21. 21.
    Lavrač N (1999) Machine learning for data mining in medicine In: Artificial intelligence in medicine. Springer, Berlin/Heidelberg, pp 47–62Google Scholar
  22. 22.
    Lomb NR (1976) Least-squares frequency analysis of unequally spaced data (in astronomy). Astrophys Space Sci 39(560):447–462CrossRefGoogle Scholar
  23. 23.
    Malik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, Schwartz PJ (1996) Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Eur Heart J 17(134):354–381Google Scholar
  24. 24.
    Merri M, Farden DC, Mottley JG, Titlebaum EL (1990) Sampling frequency of the electrocardiogram for spectral analysis of the heart rate variability. IEEE Trans Biomed Eng 37(3235):99–106CrossRefPubMedGoogle Scholar
  25. 25.
    Mietus JE, Peng CK, Henry I, Goldsmith RL, Goldberger AL (2002) The pNNx files: re-examining a widely used heart rate variability measure. Heart 88(59):378–380CrossRefPubMedGoogle Scholar
  26. 26.
    Mitov IP, Daskalov IK (1998) Comparison of heart rate variability spectra using generic relationships of their input signals. Med Biol Eng Comput 36(3170):573–580CrossRefPubMedGoogle Scholar
  27. 27.
    Musialik-Lydka A, Sredniawa B, Pasyk S (2003) Heart rate variability in heart failure. Kardiol Pol 58(1114):10–16PubMedGoogle Scholar
  28. 28.
    Pecchia L, Melillo P, Bracale M (2010) Remote health monitoring of heart failure with data mining via cart method on HRV features. IEEE Trans Biomed Eng. doi: 10.1109/TBME.2010.2092776
  29. 29.
    Pecchia L, Melillo P, Sansone M, Bracale M (2010) Discrimination power of short-term heart rate variability measures for CHF assessment. IEEE Trans Inf Technol Biomed. doi: 10.1109/TITB.2010.2091647
  30. 30.
    Rajendra Acharya U, Paul Joseph K, Kannathal N, Lim CM, Suri JS (2006) Heart rate variability: a review. Med Biol Eng Comput 44(3151):1031–1051CrossRefPubMedGoogle Scholar
  31. 31.
    Roche F, Gaspoz J-M, Court-Fortune I, Minini P, Pichot V, Duverney D, Costes F, Lacour J-R, Barthelemy J-C (1999) Screening of obstructive sleep apnea syndrome by heart rate variability analysis. Circulation 100(121):1411–1415PubMedGoogle Scholar
  32. 32.
    Smilde TDJ, van Veldhuisen DJ, van den Berg MP (2009) Prognostic value of heart rate variability and ventricular arrhythmias during 13-year follow-up in patients with mild to moderate heart failure. Clin Res Cardiol 98(4):233–239CrossRefPubMedGoogle Scholar
  33. 33.
    Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc B 36(593):111–147Google Scholar
  34. 34.
    Vallejo M, Marquez MF, Borja-Aburto VH, Cardenas M, Hermosillo AG (2005) Age, body mass index, and menstrual cycle influence young women’s heart rate variability—a multivariable analysis. Clin Auton Res 15(126):292–298CrossRefPubMedGoogle Scholar
  35. 35.
    Welch P (1967) The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust 15(5745):70–73CrossRefGoogle Scholar
  36. 36.
    Wessel N, Voss A, Kurths J, Schirdewan A, Hnatkova K, Malik M (2000) Evaluation of renormalised entropy for risk stratification using heart rate variability data. Med Biol Eng Comput 38(3165):680–685CrossRefPubMedGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2010

Authors and Affiliations

  • Paolo Melillo
    • 1
  • Roberta Fusco
    • 1
  • Mario Sansone
    • 1
  • Marcello Bracale
    • 1
  • Leandro Pecchia
    • 1
  1. 1.Department of Biomedical, Telecommunication and Electronic Engineering (DIBET)University of Naples “Federico II”NaplesItaly

Personalised recommendations