Fractal correlation property of heart rate variability in chronic obstructive pulmonary disease

International Journal of COPD

Volumn 6, Issue 1, 2011, Pages 23-28

  Carvalho, Tatiana D ^a,b   Pastre, Carlos Marcelo ^a   de Godoy, Moacir Fernandes ^c   Fereira, Celso ^b   Pitta, Fábio O ^a,d   de Abreu, Luiz Carlos ^e   Ramos, Ercy Mara Cipulo ^a   Valenti, Vitor E ^b,e   Vanderlei, Luiz Carlos Marques ^a  

^a SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

^b FEDERAL UNIVERSITY OF SÃO PAULO   (Brazil)

^c FACULDADE DE MEDICINA DE SÃO JOSÉ DO RIO PRETO   (Brazil)

^d STATE UNIVERSITY OF LONDRINA   (Brazil)

^e FACULDADE DE MEDICINA DO ABC   (Brazil)

Author keywords

Cardiology; Chronic obstructive; Heart rate; Nervous system; Pulmonary disease

Indexed keywords

AGED; ARTICLE; AUTONOMIC NERVOUS SYSTEM; CHRONIC OBSTRUCTIVE LUNG DISEASE; CLINICAL ARTICLE; CONTROLLED STUDY; CORRELATION ANALYSIS; FEMALE; FRACTAL ANALYSIS; HEART RATE VARIABILITY; HUMAN; MALE; MATHEMATICAL ANALYSIS; ADRENERGIC SYSTEM; BODY MASS; CHOLINERGIC SYSTEM; FORCED EXPIRATORY VOLUME; FORCED VITAL CAPACITY; HIGH FREQUENCY DOMAIN; LONG TERM FRACTAL EXPONENT; LOW FREQUENCY DOMAIN; LOW FREQUENCY HIGH FREQUENCY DOMAIN RATION; PATIENT MONITORING; PEAK EXPIRATORY FLOW; ROOT MEAN SQUARE OF DIFFERENCE; SHORT TERM FRACTAL EXPONENT; SPIROMETRY; STANDARD DEVIATION OF NORMAL TO NORMAL; STATISTICAL PARAMETERS; SUPINE POSITION;

AGED; BRAZIL; CASE-CONTROL STUDIES; FEMALE; FRACTALS; HEART FUNCTION TESTS; HEART RATE; HUMANS; LINEAR MODELS; LUNG; MALE; MIDDLE AGED; PULMONARY DISEASE, CHRONIC OBSTRUCTIVE; SIGNAL PROCESSING, COMPUTER-ASSISTED; SPIROMETRY; SUPINE POSITION; TIME FACTORS;

EID: 79251480059     PISSN: 11769106     EISSN: 11782005     Source Type: Journal    
DOI: 10.2147/COPD.S15099     Document Type: Article

References (33)

1. Rabe KF, Hurd S, Anzueto A, et al; Global Initiative for Chronic Obstructive Lung Disease. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med. 2007;176: 532-555.
2. Seemungal TA, Hurst JR, Wedzicha JA. Exacerbation rate, health status and mortality in COPD - a review of potential interventions. Int J Chron Obstruct Pulmon Dis. 2009;4:203-223.
3. Dourado VZ, Tanni SE, Vale SA, et al. Systemic manifestations in chronic obstructive pulmonary disease. J Bras Pneumol. 2006;32:161-171.
4. Borghi-Silva A, Arena R, Castello V, et al. Aerobic exercise training improves autonomic nervous control in patients with COPD. Respir Med. 2009;103:1503-1510.
5. Camillo CA, Pitta F, Possani HV, et al. Heart rate variability and disease characteristics in patients with COPD. Lung. 2008;186:393-401.
6. Pantoni CBF, Reis MS, Martins LEB, Catai AM, Costa D, Borgui- Silva A. Estudo da modulação autonômica da freqüência cardíaca em repouso de pacientes idosos com doença pulmonar obstrutiva crônica. Rev Bras Fisioter. 2007;11:35-41.
7. 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. Circulation. 1993;93:1043-1065.
8. Paschoal MA, Petrelluzzi KFS, Gonçalves NVO. Estudo da variabilidade da freqüência cardíaca em pacientes com doença pulmonar obstrutiva crônica. Rev Ciênc Med. 2002;11:27-37.
9. Pagani M, Lucini D, Pizzinelli P, et al. Effects of aging and of chronic obstructive pulmonary disease on RR interval variability. J Auton Nerv Syst. 1996;59:125-132.
10. Volterrani M, Scalvini S, Mazzuero G, et al. Decreased heart rate variability in patients with chronic obstructive pulmonary disease. Chest. 1996;106:1432-1437.
11. Huikuri HV, Makikallio TH, Perkiomaki J. Measurement of heart rate variability by methods based on nonlinear dynamics. J Electrocardiol. 2003;36:95-99.
12. Voss A, Kurths J, Kleiner HJ, et al. 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: 419-433.
13. Godoy MF, Takakura IT, Correa PR. Relevância da análise do comportamento dinâmico não-linear (Teoria do Caos) como elemento prognóstico de morbidade e mortalidade em pacientes submetidos à cirurgia de revascularização miocárdica. Arq Ciênc. 2005;12:167-171.
14. Tulppo MP, Kiviniemi AM, Hautala AJ, et al. Physiological background of the loss of fractal heart rate dynamics. Circulation. 2005;112: 314-319.
15. Tulppo MP, Hughson RL, Mäkikallio TH, Airaksinen KE, Seppänen T, Huikuri HV. Effects of exercise and passive head-up tilt on fractal and complexity properties of heart rate dynamics. Am J Physiol. 2001; 280:H1081-H1087.
16. Lohman TG, Roche AF, Martorell R. Anthropometric Standardization Reference Manual. Champaign: Human Kinetics Books; 1998.
17. Gamelin FX, Berthoin S, Bosquet L. Validity of the Polar S810 Heart Rate Monitor to measure R-R intervals at rest. Med Sci Sports Exerc. 2006;38:887-893.
18. Porto LGG, Junqueira Júnior LF. Comparison of time-domain short-term heart interval variability analysis using a wrist-worn heart rate monitor and the conventional electrocardiogram. PACE. 2008;32:43-51.
19. Vanderlei LC, Silva RA, Pastre CM, Azevedo FM, Godoy MF. Comparison of the Polar S810i monitor and the ECG for the analysis of heart rate variability in the time and frequency domains. Braz J Med Biol Res. 2008;41:854-859.
20. Vanderlei LC, Pastre CM, Hoshi RA, Carvalho TD, Godoy MF. Basic notions of heart rate variability and its clinical applicability. Rev Bras Cir Cardiovasc. 2009;24:205-217.
21. Pereira CAC, Neder JA. Diretrizes para testes de função pulmonar. J Pneumol. 2002;28(Suppl 3):S1-S238.
22. Niskanen JP, Tarvainen MP, Ranta-Aho PO, Karjalainen PA. Software for advanced HRV analysis. Comput Methods Programs Biomed. 2008; 76:73-81.
23. Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos. 1995;5:82-87.
24. Acharya RU, Lim CM, Joseph P. Heart rate variability analysis using correlation dimension and detrended fluctuation analysis. ITBM-RBM. 2002;23:333-339.
25. Perkiömäki JS, Zareba W, Ruta J, et al; for IDEAL Investigators. Fractal and complexity measures of heart rate dynamics after acute myocardial infarction. Am J Cardiol. 2001;88:777-781.
26. Hautala AJ, Mäkikallio TH, Seppänen T, Huikuri HV, Tulppo MP. Shortterm correlation properties of RR interval dynamics at different exercise intensity levels. Clin Physiol Funct Imaging. 2003;23:215-223.
27. Hayano J, Takahashi H, Toriyama T, et al. Prognostic value of heart rate variability during long-term follow-up in chronic haemodialysis patients with end-stage renal disease. Nephrol Dial Transplant. 1999;14:1480-1488.
28. Vanderlei LC, Pastre CM, Júnior IF, de Godoy MF. Fractal correlation of heart rate variability in obese children. Auton Neurosc. 2010; 155:125-129.
29. Vanderlei LC, Pastre CM, Freitas Júnior IF, de Godoy MF. Analysis of cardiac autonomic modulation in obese and eutrophic children. Clinics. 2010;65:789-792.
30. Mäkikallio TH, Høiber S, Køber L, et al. 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. 1999;83:836-839.
31. Makikallio TH, Seppanen T, Airaksinen KE, et al. Dynamic analysis of heart rate may predict subsequent ventricular tachycardia after myocardial infarction. Am J Cardiol. 1997;80:779-783.
32. Higgins JP. Nonlinear systems in medicine. Yale J Biol Med. 2002; 75(5-6):247-260.
33. Heffernan KS, Sosnoff JJ, Fahs CA, Shinsako KK, Jae SY, Fernhall B. Fractal scaling properties of heart rate dynamics following resistance exercise training. J Appl Physiol. 2008;105:109-113.