Fractal dynamics in physiology: Alterations with disease and aging

By Ary L. Goldberger, L. A. Amaral, Jeffrey M. Hausdorff, Plamen Ch. Ivanov, Chung-Kang Peng, H. Eugene Stanley. Proceedings of the National Academy of Sciences of the United States of America, 2002

2,159 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Molecular Biology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6622cf6e6950276e5da4063a9dbd81e161e990f9

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Citations in the corpus

Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review (2012, background): “First, biological systems are typically characterized by complex dynamics, with rich temporal structure even at rest [55].”
Multiscale entropy analysis of complex physiologic time series. (2002, background): “As a result, cardiac interbeat interval (RR) time series under healthy conditions have a complex temporal structure with multiscale correlations [1, 2].”
Colloquium: Criticality and dynamical scaling in living systems (2017, background): “The presence of temporal scale-invariance in physiological rhythms of healthy subjects, as well as its break-down in abnormal conditions, have been long explored (Bassingthwaighte et al., 1994; Goldberger et al., 2002; Losa, 1995).”
Does noise provide a basis for the unification of motor learning theories (2006, background): “In contrast in physiology, the noise in the rhythm of heart beats is associated with constructive characteristics, permitting rapid adaptation to stressors, and an individual’s heart can be diagnosed as pathological when the variability of subsequent heart beats becomes too small (Goldberger et al.,”
EEG microstate sequences in healthy humans at rest reveal scale-free dynamics (2010, background): “The most notable examples are distinct fractal properties of coding and noncoding parts of human DNA sequences (27) and changes of heartbeat dynamics related to pathological conditions (28, 29).”
Multi-scale entropy analysis of Mississippi River flow (2007, methodology): “Unlike traditional entropy-based methods that characterize the pathologic signals with single higher entropy than healthy ones, the MSE analysis showed that entropies of healthy signals are higher than pathologic ones at large time scales, corresponding to the hypothesized ‘‘complexity loss’’ with d”
Indicators for Complex Innovation Systems (2006, background): “Amongst other things a complex system • has a dynamic internal structure that evolves and interacts in a complex manner, • exhibits emergent behaviours and patterns that are not the result of rules or caused by a single entity in the system.”
Methods derived from nonlinear dynamics for analysing heart rate variability (2009, background): “This is in accordance with the previous studies (Peng et al. 1995; Voss et al. 1996; Goldberger et al. 2002), where larger dimensions and entropies implied a greater Phil.”
Detrended Fluctuation Analysis: A Scale-Free View on Neuronal Oscillations (2012, background): “…is growing evidence that physiological processes can exhibit fluctuations without characteristic scales and that this scale-free dynamics is important for their function (Bassingthwaighte et al., 1994; Bak, 1996; Goldberger et al., 2002; Stam, 2005; Ghosh et al., 2008; He et al., 2010; West, 2010).”