Long-range dependencies in heart rate signals—revisited

Abstract

The RR series extracted from human electrocardiogram signal (ECG) is considered as a fractal stochastic process. The manifestation of long-range dependencies is the presence of power laws in scale dependent process characteristics. Exponents of these laws: β -describing power spectrum decay, α -responsible for decay of detrended fluctuations or H related to, so-called, roughness of a signal, are known to differentiate hearts of healthy people from hearts with congestive heart failure. There is a strong expectation that resolution spectrum of exponents, so-called, local exponents in place of global exponents allows to study differences between hearts in details. The arguments are given that local exponents obtained in multifractal analysis by the two methods: wavelet transform modulus maxima (WTMM) and multifractal detrended fluctuation analysis (MDFA), allow to recognize the following four stages of the heart: healthy and young, healthy and advance in years, subjects with left ventricle systolic dysfunction (NYHA I-III class) and characterized by severe congestive heart failure (NYHA III-IV class).

Multifractal estimates of monofractality in RR-heart series in power spectrum ranges

“…Some other modifications in multifractal spectra caused by other heart diseases, like a change in location or curvature of a spectrum, have been also found [15,14,18,19]. In the following, we compare the fractal properties found in LF, VLF and ULF bands for RR signals of a group of healthy people to a group of people whose hearts suffer from reduced left ventricle systolic (rlvs) function.…”

“…[2][3][4][6][7][8][9][10]) which is strongly supported by physicists (see, e.g., Refs. [11][12][13][14][15][16][17][18][19]), states that the variability of the heart rate reveals a delicate balance between the two antagonistic branches of the ANS: parasympathetic and sympathetic.…”

Multifractal analysis of the drought area in seven large regions of China from 1961 to 2012

“…Following that, Kantelhardt et al (2002) extended DFA into multifractal detrended fluctuation analysis (MFDFA) which enables the multifractal behavior of data to be detected, and by studying their shuffled and surrogate time series and comparing them with the results of the original series, the sources of multifractality can be investigated (Jafari et al 2007; Kimiagar et al 2009;Lim et al 2007;Niu et al 2008;Pedram and Jafari 2008;Telesca et al 2004). MFDFA has been used to study time series in geophysics (Kantelhardt et al 2003;Kavasseri and Nagarajan 2005;Koscielny-Bunde et al 2006), physiology (Dutta 2010;Makowiec et al 2006Makowiec et al , 2011, financial markets (Oswiecimka et al 2005;Yuan et al 2009), and the exchange rates of currencies (Norouzzadeh and Rahmani 2006a, b;Oh et al 2012;Wang et al 2011a, b). Multifractals describe the dynamic characteristics of systems more carefully and comprehensively, and characterize their properties both locally and globally.…”

Epileptic Seizure: A New Approach for Quantification of Autonomic Deregulation with Chaos Based Technique

“…In addition, the application of MFDFA on pre-ictal and post-ictal ECG signals together with larger sample size can yield a better result towards identifying diagnosis, onset and prognosis. Again, in the modern scientific fields of studying different heart diseases, though MFDFA is a widely-used methodology [81][82][83] but to the best of our knowledge no study has been reported about the changes in heart rate dynamics after occurrence of seizure using MFDFA. The application of MFDFA methodology on ECG patterns can help in understanding the changes that occur in heart rate after patients have encountered seizure.…”