Journal ArticleDOI
Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series
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TLDR
A new method--detrended fluctuation analysis (DFA)--for quantifying this correlation property in non-stationary physiological time series is described and application of this technique shows evidence for a crossover phenomenon associated with a change in short and long-range scaling exponents.Abstract:
The healthy heartbeat is traditionally thought to be regulated according to the classical principle of homeostasis whereby physiologic systems operate to reduce variability and achieve an equilibrium-like state [Physiol. Rev. 9, 399-431 (1929)]. However, recent studies [Phys. Rev. Lett. 70, 1343-1346 (1993); Fractals in Biology and Medicine (Birkhauser-Verlag, Basel, 1994), pp. 55-65] reveal that under normal conditions, beat-to-beat fluctuations in heart rate display the kind of long-range correlations typically exhibited by dynamical systems far from equilibrium [Phys. Rev. Lett. 59, 381-384 (1987)]. In contrast, heart rate time series from patients with severe congestive heart failure show a breakdown of this long-range correlation behavior. We describe a new method--detrended fluctuation analysis (DFA)--for quantifying this correlation property in non-stationary physiological time series. Application of this technique shows evidence for a crossover phenomenon associated with a change in short and long-range scaling exponents. This method may be of use in distinguishing healthy from pathologic data sets based on differences in these scaling properties.read more
Citations
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Significance of the Accuracy of RR Interval Detection for the Analysis of New Dynamic Measures of Heart Rate Variability
TL;DR: This work has shown that the validity of several new methods introduced for studying complex heart rate dynamics from Holter recordings is not well established, and these methods give different, complementary information on heart rate behavior compared to the traditional measures of HRV.
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Analytical and empirical fluctuation functions of the EEG microstate random walk - Short-range vs. long-range correlations
TL;DR: The analysis suggests that the scaling behaviour of resting state EEG microstate sequences, though markedly different from uncorrelated, zero-order Markov processes, can often not be distinguished from a short-range correlated, first- order Markov process.
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Application of statistical physics methods and conceptsto the study of science & technology systems
Luís A. Nunes Amaral,Parameswaran Gopikrishnan,Kaushik Matia,Vasiliki Plerou,Harry Eugene Stanley +4 more
TL;DR: It is hypothesize that the proposed methods may be particularly useful for fields of S&T for which either not enough information is available, or for which evolution is so fast that there is not enough time to collect enough data to make an informed decision.
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Modeling neuronal avalanches and long-range temporal correlations at the emergence of collective oscillations: continuously varying exponents mimic M/EEG results
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Journal ArticleDOI
SCD-HeFT: Use of R-R interval statistics for long-term risk stratification for arrhythmic sudden cardiac death
Wan-Tai M Au-Yeung,Per G. Reinhall,Jeanne E. Poole,Jill Anderson,George Johnson,Ross D. Fletcher,Hans J Moore,Daniel B. Mark,Kerry L. Lee,Gust H. Bardy +9 more
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Long-range correlations in nucleotide sequences
Chung-Kang Peng,Sergey V. Buldyrev,Ary L. Goldberger,Shlomo Havlin,Shlomo Havlin,Francesco Sciortino,Michael Simons,Michael Simons,H. E. Stanley +8 more
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Journal ArticleDOI
Long-range anticorrelations and non-Gaussian behavior of the heartbeat
Chung-Kang Peng,Chung-Kang Peng,Chung-Kang Peng,J. Mietus,J. Mietus,J. Mietus,Jeffrey M. Hausdorff,Jeffrey M. Hausdorff,Jeffrey M. Hausdorff,Shlomo Havlin,Shlomo Havlin,Shlomo Havlin,H. E. Stanley,H. E. Stanley,H. E. Stanley,Ary L. Goldberger,Ary L. Goldberger,Ary L. Goldberger +17 more
TL;DR: It is found that the successive increments in the cardiac beat-to-beat intervals of healthy subjects display scale-invariant, long-range anticorrelations (up to 10(4) heart beats), and the different scaling behavior in health and disease must relate to the underlying dynamics of the heartbeat.