Abstract
Congestive heart failure (CHF) is a major medical challenge in developed countries. In order to screen patients with CHF and healthy subjects during circadian observation, accurate judgment and fast response are imperative. In this study, optimal timing during circadian observation via the heart rate variability (HRV) was sought. We tested 29 CHF patients and 54 healthy subjects in the control group from the interbeat interval databases of PhysioBank. By invoking the α1 parameter in detrended fluctuation analysis of HRV, we found that it could be used as an indicator to screen the patients with CHF and subjects in normal sinus rhythm (NSR) under Kruskal–Wallis test. By invoking Fano factor, the optimal timing to screen CHF patients and healthy subjects was found to be from 7 PM to 9 PM during the circadian observation. In addition, this result is robust in a sense that the same result can be achieved by using different ECG recording lengths of 2, 5, 10, … , and 120 min, respectively. Furthermore, a support vector machine was employed to classify CHF and NSR with α1 parameter of a moving half-hour ECG recordings via leave-one-out cross validation. The results showed that the superlative screening performance was obtained in the 7 pm–9 pm period during circadian observation. It is believed that this result of optimal timing will be helpful in the non-invasive monitoring and screening of CHF patients and healthy subjects in the clinical practice.
Similar content being viewed by others
Abbreviations
- CHF:
-
Congestive heart failure
- CHFRRID:
-
Congestive heart failure RR interval database
- DFA:
-
Detrended fluctuation analysis
- HRV:
-
Heart rate variability
- LOOCV:
-
Leave-one-out cross validation
- NSR:
-
Normal sinus rhythm
- NSRRRID:
-
Normal sinus rhythm RR interval database
- ROC:
-
Receiver operating characteristic
- SVM:
-
Support vector machine
References
Allegra, J. R., D. G. Cochrane, and R. Biglow. Monthly, weekly, and daily patterns in the incidence of congestive heart failure. Acad. Emerg. Med. 8(6):682–685, 2001.
Aschoff, J., S. Daan, and G. A. Groos. Vertebrate Circadian Systems Structure and Physiology. New York: Springer-Verlag, 1982.
Bär, K. J., M. Koschke, S. Berger, S. Schulz, M. Tancer, A. Voss, and V. K. Yeragani. Influence of olanzapine on QT variability and complexity measures of heart rate in patients with schizophrenia. J. Clin. Psychopharmacol. 28(6):694–698, 2008.
Barash, D., R. A. Silverman, P. Gennis, et al. Circadian variation in the frequency of myocardial infarction and death associated with acute pulmonary edema. J. Emerg. Med. 7:119–121, 1989.
Beckers, F., B. Verheyden, K. Couckuyt, and A. E. Aubert. Fractal dimension in health and heart failure. Biomed. Tech. 51(4):194–197, 2006.
Bernardi, L., L. Ricordi, P. Lazzari, et al. Impaired circadian modulation of sympathovagal activity in diabetes—a possible explanation for altered temporal onset of cardiovascular disease. Circulation 86:1443–1452, 1992.
Bigger, J. T., L. F. Fleiss, R. C. Steinman, et al. RR variability in healthy, middle-age persons compared with patients with chronic coronary heart disease or recent acute myocardial infarction. Circulation 91:1936–1943, 1995.
Blaber, A. P., R. L. Bondar, and R. Freeman. Coarse graining spectral analysis of HR and BP variability in patients with autonomic failure. Am. J. Physiol. 271(4):H1555–H1564, 1996.
Blesić, S., D. Stratimirović, S. Milosević, and M. Ljubisavljević. Detecting long-range correlations in time series of dorsal horn neuron discharges. Ann. N. Y. Acad. Sci. 1048:385–391, 2005.
Butler, G. C., S. Ando, and J. S. Floras. Fractal component of variability of heart rate and systolic blood pressure in congestive heart failure. Clin. Sci. 92(6):543–550, 1997.
Chang, S., M. C. Hsyu, H. Y. Cheng, S. H. Hsieh, and C. C. Lin. Synergic co-activation in forearm pronation. Ann. Biomed. Eng. 36(12):2002–2018, 2008.
Chang, S., S. J. Li, M. J. Chiang, S. J. Hu, and M. C. Hsyu. Fractal dimension estimation via spectral distribution function and its application to physiological signals. IEEE Trans. Biomed. Eng. 54(10):1895–1898, 2007.
Cohen, M. C., K. M. Rohtla, C. E. Lavery, et al. Meta-analysis of the morning excess of acute myocardial infarction and sudden cardiac death. Am. J. Cardiol. 79(11):1512, 1997.
Echeverría, J. C., S. D. Aguilar, M. R. Ortiz, J. Alvarez-Ramirez, and R. González-Camarena. Comparison of RR-interval scaling exponents derived from long and short segments at different wake periods. Physiol. Meas. 27(4):N19–N25, 2006.
Elliott, W. J. Circadian variation in the timing of stroke onset: a meta-analysis. Stroke 29:992–996, 1998.
Fano, U. Ionization yield of radiations. II. The fluctuations of the number of ions. Phys. Rev. 72:26–29, 1947.
Fava, S., and J. Azzopardi. Circadian variation in the onset of acute pulmonary edema and associated acute myocardial infarction in diabetic and nondiabetic patients. Am. J. Cardiol. 80:336–338, 1997.
Fava, S., J. Azzopardi, H. A. Muscat, et al. Absence of circadian variation in the onset of acute myocardial infarction in diabetic subjects. Br. Heart J. 74:370–372, 1995.
Feirwals, B. J., and L. E. Toothake. Empirical comparison of ANOVA F-test, normal scores test and Kruskal–Wallis test under violation of assumptions. Educ. Psychol. Meas. 34(4):789–799, 1974.
Freitas, J., and F. Rocha-Goncalves. Circadian heart rate variability and blood pressure pattern in severe autonomic failure. J. Hyperten. 22:S22, 2004.
Hu, K., F. A. Scheer, R. M. Buijs, and S. A. Shea. The endogenous circadian pacemaker imparts a scale-invariant pattern of heart rate fluctuations across time scales spanning minutes to 24 hours. J. Biol. Rhythms 23(3):265–273, 2008.
Hurst, H. E. Long term storage capacity of reservoirs. Trans. Am. Soc. Civ. Eng. 116:770–799, 1951.
Ivanov, P. C., L. A. Amaral, A. L. Goldberger, S. Havlin, M. G. Rosenblum, Z. R. Struzik, and H. E. Stanley. Multifractality in human heartbeat dynamics. Nature 399(6735):461–465, 1999.
Krumholz, H. M., Y. T. Chen, Y. Wang, V. Vaccarino, M. J. Radford, and R. I. Horwitz. Predictors of readmission among elderly survivors of admission with heart failure. Am. Heart J. 139:72–77, 2000.
Levy, R. D., D. Cunningham, L. M. Shapiro, et al. Diurnal variation in left-ventricular function—a study of patients with myocardial–ischemia, syndrome X, and of normal controls. Br. Heart J. 57(2):148–153, 1987.
Moody, G. B., R. G. Mark, and A. L. Goldberger. PhysioNet: a web-based resource for the study of physiologic signals. IEEE Eng. Med. Biol. Mag. 20(3):70–75, 2001.
Mudd, J. O., and D. A. Kass. Tackling heart failure in the twenty-first century. Nature 451(7181):919–928, 2008.
Nakamura, Y., Y. Yamamoto, and I. Muraoka. Autonomic control of heart rate during physical exercise and fractal dimension of heart rate variability. J. Appl. Physiol. 74(2):875–881, 1993.
Neubauer, S. The failing heart: an engine out of fuel. N. Engl. J. Med. 356(11):1140–1151, 2007.
Otsuka, K., G. Cornélissen, and F. Halberg. Circadian rhythmic fractal scaling of heart rate variability in health and coronary artery disease. Clin. Cardiol. 20(7):631–638, 1997.
Panina, G., U. N. Khot, E. Nunziata, R. J. Cody, and P. F. Binkley. Assessment of autonomic tone over a 24-hour period in patients with congestive heart failure: relation between mean heart rate and measures of heart rate variability. Am. Heart J. 129(4):748–753, 1995.
Peng, C. K., S. V. Buldyrev, S. Havlin, M. Simons, H. E. Stanley, and A. L. Goldberger. Mosaic organization of DNA nucleotides. Phys. Rev. E. 49(2):1685–1689, 1994.
Peng, C. K., S. Havlin, H. E. Stanley, and A. L. Goldberger. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5(1):82–87, 1995.
Peng, C. K., J. Mietus, J. M. Hausdorff, S. Havlin, H. E. Stanley, and A. L. Goldberger. Long-range anticorrelations and non-Gaussian behavior of the heartbeat. Phys. Rev. Lett. 70(9):1343–1346, 1993.
Perkiömäki, J. S., T. H. Mäkikallio, and H. V. Huikuri. Fractal and complexity measures of heart rate variability. Clin. Exp. Hypertens. 27(2):149–158, 2005.
Poon, C. S., and C. K. Merrill. Decrease of cardiac chaos in congestive heart failure. Nature 389(6650):492–495, 1997.
Pursiainen, V., T. H. Haapaniemi, J. T. Korpelainen, H. V. Huikuri, K. A. Sotaniemi, and V. V. Myllylä. Circadian heart rate variability in Parkinson’s disease. J. Neurol. 249(11):1535–1540, 2002.
Ridha, M., T. H. Mäkikallio, G. Lopera, J. Pastor, E. de Marchena, S. Chakko, H. V. Huikuri, A. Castellanos, and R. J. Myerburg. Effects of carvedilol on heart rate dynamics in patients with congestive heart failure. Ann. Noninvasive Electrocardiol. 7(2):133–138, 2002.
Rosamond, W., K. Flegal, G. Friday, K. Furie, A. Go, K. Greenlund, N. Haase, M. Ho, V. Howard, B. Kissela, S. Kittner, D. Lloyd-Jones, M. McDermott, J. Meigs, C. Moy, G. Nichol, C. J. O’Donnell, V. Roger, J. Rumsfeld, P. Sorlie, J. Steinberger, T. Thom, S. Wasserthiel-Smoller, Y. Hong, and American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2007 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 115(5):e69–e171, 2007.
Scanaill, C. N., S. Carew, P. Barralon, N. Noury, D. Lyons, and G. M. Lyons. A review of approaches to mobility telemonitoring of the elderly in their living environment. Ann. Biomed. Eng. 34(4):547–563, 2006.
Telesca, L., G. Colangelo, V. Lapenna, and M. Macchiato. Fluctuation dynamics in geoelectrical data: an investigation by using multifractal detrended fluctuation analysis. Phys. Lett. A 332:398–404, 2004.
Turcott, R. G., and M. C. Teich. Fractal character of the electrocardiogram: distinguishing heart-failure and normal patients. Ann. Biomed. Eng. 24(2):269–293, 1996.
Van Leeuwen, P., H. Bettermann, U. An der Heiden, and H. C. Kümmell. Circadian aspects of apparent correlation dimension in human heart rate dynamics. Am. J. Physiol. 269(1):H130–H134, 1995.
Vapnik, V., and A. Chervonenkis. Theory of Pattern Recognition: Statistical Problems of Learning. Moscow: Nauka, 1974.
Vapnik, V., and A. Lerner. Pattern recognition using generalized portrait method. Automat. Remote Control 24:774–780, 1963.
Yamamoto, Y., J. O. Fortrat, and R. L. Hughson. On the fractal nature of heart rate variability in humans: effects of respiratory sinus arrhythmia. Am. J. Physiol. 269(2):480–486, 1995.
Yamamoto, Y., Y. Nakamura, H. Sato, M. Yamamoto, K. Kato, and R. L. Hughson. On the fractal nature of heart rate variability in humans: effects of vagal blockade. Am. J. Physiol. 269(4):R830–R837, 1995.
Yeragani, V. K., K. Srinivasan, S. Vempati, R. Pohl, and R. Balon. Fractal dimension of heart rate time series: an effective measure of autonomic function. J. Appl. Physiol. 75(6):2429–2438, 1993.
Acknowledgments
This study was supported in part by the Grant NSC94-2213-E-007-094 from National Science Council, Taiwan, and the Grant V99C1-106 from Taipei Veterans General Hospital, Taipei, Taiwan. We are grateful to the reviewers for their comments and suggestions that have improved the robustness of this article a great deal.
Author information
Authors and Affiliations
Corresponding author
Additional information
Associate Editor Ioannis A. Kakadiaris oversaw the review of this article.
Rights and permissions
About this article
Cite this article
Jong, TL., Chang, B. & Kuo, CD. Optimal Timing in Screening Patients with Congestive Heart Failure and Healthy Subjects During Circadian Observation. Ann Biomed Eng 39, 835–849 (2011). https://doi.org/10.1007/s10439-010-0180-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10439-010-0180-6