Nonlinear analysis of heart rate variability of healthy subjects and patients with chronic obstructive pulmonary disease

References

• Global Initiative for Chronic Obstructive Lung Disease [Internet]. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. 2016 [cited 2016 Dec 15]. Available from: http://goldcopd.org/global-strategy-diagnosis-management-prevention-copd-2016/
   Google Scholar
• Borghi-Silva A, Arena R, Castello V. Aerobic exercise training improves autonomic nervous control in patients with COPD. Respir Med. 2009;103:1503–1510.
   PubMed Web of Science ®Google Scholar
• Carvalho TD, Pastre CM, de Godoy MF. Fractal correlation property of heart rate variability in chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2011;6:23–28.
   PubMed Web of Science ®Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Voss A, Schulz S, Schroeder R, et al. Methods derived from nonlinear dynamics for analysing heart rate variability. Philos Trans A Math Phys Eng Sci. 2009;367:277–296.
   PubMed Web of Science ®Google Scholar
• Lombardi F. Chaos theory, heart rate variability, and arrhythmic mortality. Circulation. 2000;101:8–10.
   PubMed Web of Science ®Google Scholar
• Neves VR, Takahashi ACM, do Santos-Hiss MDB, et al. Linear and nonlinear analysis of heart rate variability in coronary disease. Clin Auton Res. 2012;22:175–183.
   PubMed Web of Science ®Google Scholar
• Rossi RC, Vanderlei FM, Bernardo AF, et al. Effect of pursed-lip breathing in patients with COPD: linear and nonlinear analysis of cardiac autonomic modulation. COPD. 2014;11:39–45.
   PubMedGoogle Scholar
• Vanderlei LCM, Pastre CM, Freitas IF, et al. Geometric indexes of heart rate variability in obese and eutrophic children. Arq Bras Cardiol. 2010;95:35–40.
   PubMed Web of Science ®Google Scholar
• Koichubekov BK, Sorokina MA, Laryushina YM, et al. Nonlinear analyses of heart rate variability in hypertension. Ann Cardiol Angeiol. 2018;67(3):174–179.
   Google Scholar
• Nejadgholi I, Moradi MH, Abdolali F. Using phase space reconstruction for patient independent heartbeat classification in comparison with some benchmark methods. Comput Biol Med. 2011;41:411–419.
   PubMed Web of Science ®Google Scholar
• Diaz J, Gonzalez C, Escalona O. Nonlinear analysis of the ECG during atrial fibrillation in patients for low energy internal cardioversion. Proceedings of the 30th Annual International Conf Proc IEEE Eng Med Biol Soc. 2008;2008:1619–1622.
   Google Scholar
• Acharya UR, Faust O, Kadri NA, et al. Automated identification of normal and diabetes heart rate signals using nonlinear measures. Comput Biol Med. 2013;43:1523–1529.
   PubMed Web of Science ®Google Scholar
• Goudis CA, Konstantinidis AK, Ntalas IV, et al. Electrocardiographic abnormalities and cardiac arrhythmias in chronic obstructive pulmonary disease. Int J Cardiol. 2015;199:264–273.
   PubMed Web of Science ®Google Scholar
• Mazzuco A, Medeiros WM, Sperling MPR, et al. Relationship between linear and nonlinear dynamics of heart rate and impairment of lung function in COPD patients. Int J Chron Obstruct Pulmon Dis. 2015;10:1651–1661.
   PubMed Web of Science ®Google Scholar
• Goulart CDL, Simon JC, Schneiders PDB, et al. Respiratory muscle strength effect on linear and nonlinear heart rate variability parameters in COPD patients. COPD. 2016;11:1671–1677.
   Google Scholar
• Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng. 1985;32:230–236.
   PubMed Web of Science ®Google Scholar
• Acharya UR, Faust O, Sree V, et al. Linear and nonlinear analysis of normal and CAD-affected heart rate signals. Comput Methods Programs Biomed. 2014;113:55–68.
   PubMed Web of Science ®Google Scholar
• Takens F. Detecting strange attractors in turbulence. Lect Notes Math. 1981;898:366.
   Google Scholar
• Cao L. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D. 1997;110:43–50.
   Web of Science ®Google Scholar
• Fraser AM, Swinney HL. Independent coordinates for strange attractors from mutual information. Phys Rev A Gen Phys. 1986;33:1134–1140.
   PubMedGoogle Scholar
• Abarbanel HDI, Brown R, Sidorowich JJ, et al. The analysis of observed chaotic data in physical systems. Rev Mod Phys. 1993;65:1331–1392.
   Web of Science ®Google Scholar
• Haykin S, Li XB. Detection of signals in chaos. IEEE. 1995;83:95–122.
   Web of Science ®Google Scholar
• Rosenstein MT, Collins JJ, de Luca CJ. A practical method for calculating largest Lyapunov exponents from small data sets. Physica D. 1993;65:117–134.
   Web of Science ®Google Scholar
• Strogatz SH. Nonlinear dynamics and chaos. Reading (MA): Perseus Books Publishing; 1994.
   Google Scholar
• Grassberger P, Procaccia I. Characterization of strange attractors. Phys Rev Lett. 1983;50:346–349.
   Web of Science ®Google Scholar
• Correa PR, Catai AM, Takakura IT, et al. Heart rate variability and pulmonary infections after myocardial revascularization. Arq Bras Cardiol. 2010;95:448–456.
   PubMed Web of Science ®Google Scholar
• Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA. 1991;88:2297–2301.
   PubMed Web of Science ®Google Scholar
• Valenza G, Allegrini P, Lanata A, et al. Dominant Lyapunov exponent and approximate entropy in heart rate variability during emotional visual elicitation. Front Neuroeng. 2012;5:1–7.
   PubMedGoogle Scholar
• Koichubekov B, Korshukov I, Omarbekova N, et al. Computation of nonlinear parameters of heart rhythm using short time ECG segments. Comput Math Methods Med. 2015;2015:983479.
   PubMed Web of Science ®Google Scholar
• Pivatelli FC, dos Santos MA, Fernandes GB, et al. Sensitivity, specificity and predictive values of linear and nonlinear indices of heart rate variability in stable angina patients. Int Arch Med. 2012;5:31.
   PubMedGoogle Scholar
• Acharya UR, Faust O, Kannathal N, et al. Non-linear analysis of EEG signals at various sleep stages. Comput Methods Programs Biomed. 2005;80:37–45.
   PubMed Web of Science ®Google Scholar
• Yılmaz D, Güler NF. Analysis of the Doppler signals using largest Lyapunov exponent and correlation dimension in healthy and stenosed internal carotid artery patients. Digit Signal Process. 2010;20:401–409.
   Web of Science ®Google Scholar
• Koulaouzidis G, Das S, Cappiello G, et al. Prompt and accurate diagnosis of ventricular arrhythmias with a novel index based on phase space reconstruction of ECG. Int J Cardiol. 2015;182:38–43.
   PubMed Web of Science ®Google Scholar
• Tsonis A, Chaos: from theory to applications. New York: Premium Press; 1992.
   Google Scholar
• Wolf A, Swift JB, Swinney HL, et al. Determining Lyapunov exponents from a time series. Physica. 1985;16:285–317.
   Google Scholar
• Hu J, Gao J, Tung WW. Characterizing heart rate variability by scale-dependent Lyapunov exponent. Chaos. 2009;19:028506.
   PubMed Web of Science ®Google Scholar
• Zamarron C, Hornero R, del Campo F, et al. Heart rate regularity analysis obtained from pulse oximetric recordings in the diagnosis of obstructive sleep apnea. Sleep Breath. 2006;10:83–89.
   PubMed Web of Science ®Google Scholar
• Valentini M, Parati G. Variables influencing heart rate. Prog Cardiovasc Dis. 2009;52:11–19.
   PubMed Web of Science ®Google Scholar