Heart rate variability and nonlinear dynamic analysis in patients with stress-induced cardiomyopathy

Medical and Biological Engineering and Computing

Volumn 50, Issue 10, 2012, Pages 1037-1046

  Krstacic, Goran ^a   Parati, Gianfranco ^b   Gamberger, Dragan ^c   Castiglioni, Paolo ^d   Krstacic, Antonija ^e   Steiner, Robert ^f  

^a Institute for Cardiovascular Diseases and Rehabilitation   (Croatia)

^b UNIVERSITY OF MILANO BICOCCA   (Italy)

^c RUDER BO KOVI INSTITUTE   (Croatia)

^d IRCCS FONDAZIONE DON CARLO GNOCCHI   (Italy)

^e SESTRE MILOSRDNICE UNIVERSITY HOSPITAL   (Croatia)

^f UNIVERSITY HOSPITAL OSIJEK   (Croatia)

Author keywords

Chaos theory; Heart rate variability; Nonlinear dynamics; Stress induced cardiomyopathy; Takotsubo syndrome

Indexed keywords

AFTER-TREATMENT; APPROXIMATE ENTROPY; CONTROL GROUPS; DETRENDED FLUCTUATION ANALYSIS; DIGITAL SAMPLING; ECG RECORDING; HEALTHY SUBJECTS; HEART RATE VARIABILITY; HURST EXPONENTS; INTERVAL VALUE; LONG RANGE CORRELATIONS; PROGNOSTIC SIGNIFICANCE; RR INTERVALS; STRESS-INDUCED; TAKOTSUBO SYNDROME;

CHAOS THEORY; DYNAMICS; ELECTROCARDIOGRAPHY; ENTROPY; FRACTALS; HEART; NONLINEAR ANALYSIS; PATIENT REHABILITATION; PATIENT TREATMENT;

TIME SERIES ANALYSIS;

AGED; ARTICLE; CASE CONTROL STUDY; ELECTROCARDIOGRAPHY; FEMALE; FRACTAL ANALYSIS; HEART ARRHYTHMIA; HOSPITALIZATION; HUMAN; METHODOLOGY; MIDDLE AGED; NONLINEAR SYSTEM; PROGNOSIS; SIGNAL PROCESSING; TAKOTSUBO CARDIOMYOPATHY;

AGED; ARRHYTHMIAS, CARDIAC; CASE-CONTROL STUDIES; ELECTROCARDIOGRAPHY; FEMALE; FRACTALS; HOSPITALIZATION; HUMANS; MIDDLE AGED; NONLINEAR DYNAMICS; PROGNOSIS; SIGNAL PROCESSING, COMPUTER-ASSISTED; TAKOTSUBO CARDIOMYOPATHY;

EID: 84867887828     PISSN: 01400118     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11517-012-0947-z     Document Type: Article

References (41)

1. Bassingthwaighte JB, Raymond GM (1994) Evaluating rescaled range analysis for time series. Ann Biomed Eng 22(4):434-444
2. Brouwer J, Van Veldhuisen DJ, Man in't Veld AJ, Haaksma J, Dijk A, Visser KR, Boomsma F, Dunselman PHJM, Lie KI, and for the Dutch Ibopamine Multicentre Trial Study Group (1996) Prognostic value of heart rate variability during long-term follow-up in patients with mild to moderate heart failure. J Am Coll Cardiol 28:1183-1189
3. Castiglioni P, Parati G, Cividjian A, Quintin L, Di Rienzo M (2009) Local scale exponents of blood pressure and heart rate variability by detrended fluctuation analysis: effect of posture, exercise, and aging. IEEE Trans Biomed Eng 56(3):675-684
4. Castiglioni P, Parati G, Di Rienzo M, Carabalona R, Cividjian A, Quintin L (2011) Scale exponents of blood pressure and heart rate during autonomic blockade as assessed by detrended fluctuation analysis. J Physiol 589(2):355-369
5. Data Acquisition and Communication (2002) http:/www.iotech.com
6. DePetrillo PB, Speers D, Ruttiman UE (1999) Determining the Hurst Exponent of fractal time series and its application to electrocardiographic analysis. Comput Biol Med 29:393-406
7. Derrick D (2009) The Broken Heart syndrome: understanding Takotsubo cardiomyopathy. Critical Care Nurse 29:49-57
8. Fischer R, Akay M (1996) A comparison of analytic methods for the study of fractional Brownian motion. Ann Biomed Eng 24: 537-543
9. Glass L (1999) Chaos and heart rate variability. J Cardiovasc Electrophysiol 10:1358-1360
10. Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PCH, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):215-220
11. Goldberger AL (1996) Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at bedside. Lancet 347: 1312-1314
12. Hausdorff JM, Peng CK, Ladin Z, Wei JY, Goldberger AL (1995) Is walking a random walk? Evidence for long-range correlations in the stride interval of human gait. J Appl Physiol 78:349-358
13. Heitmann A, Huebner T, Schroeder R, Perz S, Voss A (2011) Multivariate short-term heart rate variability: a pre-diagnostic tool for screening heart disease. Med Biol Eng Comput. 49(1): 41-50 PMID: 21140234
14. Huikuri HV (1995) Heart rate variability in coronary artery disease. J Intern Med 237:349-357
15. Huikuri HV, Makikallio TH, Perkiomaki J (2003) Measurement of heart rate variability by methods based on nonlinear dynamics. Electrocardiology 36(Suppl):95-99
16. Knežević A, Martinis M, Krstačić G, Vargović E (2005) Changes in multifractal properties for stable angina pectoris. Physica A 358(2-4):505-515
17. Krstacic G (2002) Dissertation: non-linear dynamics in short time series in patient with stable coronary heart disease. Zagreb University School of Medicine, Zagreb
18. Krstacic G, Krstacic A, Smalcelj A, Milicic D, Jembrek-Gostovic M (2007) The Chaos theory and non-linear dynamics in heart rate variability analysis: does it work in short time series in patients with coronary heart disease? Annals Noninvasive Electrocardiol 12(2):130-136
19. Makikkalio TM, Ristimae T, Airaksinen AE, Peng CK, Gold-berger AL, Huikuri HV (1998) Heart rate dynamics in patients with stable angina and utility of fractal and complexity measures. Am J Cardiol 81:27-31
20. Makikallio TH, Hoiber S, Kober L, Torp-Pedersen C, Peng CK, Goldberger AL, Huikuri H, TRACE Investigators (1999) Fractal analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction. Am J Cardiol 83:836-839
21. Martinis M, Knežević A, Krstačić G, Vargović E (2004) Changes in the Hurst exponent of heartbeat intervals during physical activity. Phys Rev E 70(1):12903
22. Melillo P, Fusco R, Sansone M, Bracale M, Pecchia L (2011) Discrimination power of long-term heart rate variability measures for chronic heart failure detection. Med Biol Eng Comput. 49(1):67-74 PMID: 21203855
23. Novo S, Akashi Y, Arbustini A, Assennato P, Azzarelli S, Barbaro G, Fazio G, Fedele F, Giordan M, Mazzarotto P, Modena MG, Novo G, Parodi G, Rapezzi C, Sconci F, Sganzerla P, Tona F, Salerno-Uriarte JA (2008) Takotsubo cardiomyopathy: a consensus document. G Ital Cardiol 9:785-797
24. Novo S, Carità P, Fazio G, Novo G (2010) Takotsubo cardiomyopathy. An article from the E-Journal of the ESC council for cardiology practice. E-Journal 8(39), 05 July 2010
25. Orini M, Bailón R, Enk R, Koelsch S, Mainardi L, Laguna P (2010) A method for continuously assessing the autonomic response to music-induced emotions through HRV analysis. Med Biol Eng Comput 48(5):423-433
26. Ortak J, Khattab K, Barantke M, Wiegand UK, Bänsch D, Ince H, Nienaber CA, Bonnemeier H (2009) Evolution of cardiac autonomic nervous activity indices in patients presenting with transient left ventricular apical ballooning. Pacing Clin Electrophysiol 32(Suppl. 1):S21-S25
27. Pagani M, Montano N, Porta A, Malliani A, Abboud FM, Birkett C, Somers VK (1997) Relationship between spectral components of cardiovascular variability and direct measures of muscle sympathetic nerve activity in humans. Circulation 95: 1441-1448
28. Parati G, Murray E (2012) The human sympathetic nervous system: its relevance in hypertension and heart failure. Eur Heart J 33:1058-1066
29. Parati G, Saul JP, Di Rienzo M, Mancia G (1995) Spectral analysis of blood pressure and heart rate variability in evaluating cardiovascular regulation. A critical appraisal. Hypertension 25(6):276-286
30. Peña MA, Echeverría JC, García MT, González-Camarena R (2009) Applying fractal analysis to short sets of heart rate variability data. Med Biol Eng Comput. 47(7):709-717
31. Peng CK, Havlin S, Stanley HE, Goldberger AL (1995) Quantification of scaling exponents and crossover phenomena in non-stationary heartbeat time series. Chaos 5:82-87
32. Pincus SM, Viscarello RR (1992) Approximate entropy: a regularity statistic for fetal heart rate analysis. Obst Gynecol 79: 249-255
33. Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88:2297-2301
34. Pincus SM, Huang WM (1992) Approximate entropy: statistical properties and applications. Commun Stat Theory Method 21: 3061-3077
35. Pincus SM, Goldberger AL (1994) Physiologic time-series analysis: what does regularity quantify? Am J Physiol 226:H1643-H1656
36. Regnante RA, Zuzek RW, Weinseir S, Latif SR, Linsky RA, Ahmed HN, Sadiq I (2009) Clinical characteristics and 4-year outcomes of patients in the Rhode Island Takostsubo cardiomy-opathy. Am J Cardiol 103:1015-1019
37. Saul JP, Albrecht P, Berger RD, Cohen RJ (1987) Analysis of long-term heart rate variability: methods, 1/f scaling and implications. In: Computers in cardiology. Silver Spring. IEEE Computer Society Press, Baltimore, pp 419-422
38. Saermark K, Moeller M, Hintze U, Moelgaard H, Bloch-Thomsen PE, Huikuri H, Makikallio T, Levitan J, Lewkowicz M (2000) Comparison of recent methods of analysing heart rate variability. Fractal 8:315-322
39. Sharkey SW, Windenburg DC, Lesser JR, Maron MS, Hauser RG, Lesser JN, Haas TS, Hodges JS, Maron BJ (2010) Natural history and expansive clinical profile of stress (tako-tsubo) cardiomyopathy. J Am Coll Cardiol 55(4):333-341
40. Task Force Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996) Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 93:1043-1065
41. Wittstein IS, Thiemann DR, Lima JA, Baughman KL, Schulman SP, Gerstenblith G, Wu KC, Bivalacqua TJ, Champion HC (2005) Neurohormonal features of myocardial stunning due to sudden emotional stress. New Engl J Med 352:539-548