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Fractal dynamics in physiology: Alterations with disease and aging

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TLDR
Application of fractal analysis may provide new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as to monitoring the aging process, and similar approaches show promise in assessing other regulatory systems, such as human gait control in health and disease.
Abstract
According to classical concepts of physiologic control, healthy systems are self-regulated to reduce variability and maintain physiologic constancy. Contrary to the predictions of homeostasis, however, the output of a wide variety of systems, such as the normal human heartbeat, fluctuates in a complex manner, even under resting conditions. Scaling techniques adapted from statistical physics reveal the presence of long-range, power-law correlations, as part of multifractal cascades operating over a wide range of time scales. These scaling properties suggest that the nonlinear regulatory systems are operating far from equilibrium, and that maintaining constancy is not the goal of physiologic control. In contrast, for subjects at high risk of sudden death (including those with heart failure), fractal organization, along with certain nonlinear interactions, breaks down. Application of fractal analysis may provide new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as to monitoring the aging process. Similar approaches show promise in assessing other regulatory systems, such as human gait control in health and disease. Elucidating the fractal and nonlinear mechanisms involved in physiologic control and complex signaling networks is emerging as a major challenge in the postgenomic era.

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Journal ArticleDOI

EEG dynamics in patients with Alzheimer's disease

TL;DR: EEG abnormalities of AD patients are characterized by slowed mean frequency, less complex activity, and reduced coherences among cortical regions, suggesting that the EEG has utility as a valuable tool for differential and early diagnosis of AD.
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Gait variability: methods, modeling and meaning

TL;DR: Nine reports on the results of recent investigations into gait variability provide new insights into the factors that regulate the stride-to-stride fluctuations in walking and pave the way for expanded research into the control of gait and the practical application of measures of gact variability in the clinical setting.
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Gait dynamics, fractals and falls: finding meaning in the stride-to-stride fluctuations of human walking.

TL;DR: Support for the idea that gait dynamics has meaning has meaning and may be useful in providing insight into the neural control of locomotion and for enhancing functional assessment of aging, chronic disease, and their impact on mobility is discussed.
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Introduction to Multifractal Detrended Fluctuation Analysis in Matlab

TL;DR: The main aim of the tutorial is to give the reader a simple self-sustained guide to the implementation of MFDFA and interpretation of the resulting multifractal spectra.
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Optimal movement variability: a new theoretical perspective for neurologic physical therapy.

TL;DR: This work contends that mature motor skills and healthy states are associated with an optimal amount of movement variability, which has form and is characterized by a chaotic structure, and proposes a new model to explain movement variability as it relates to motor learning and health.
References
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Book

The Fractal Geometry of Nature

TL;DR: This book is a blend of erudition, popularization, and exposition, and the illustrations include many superb examples of computer graphics that are works of art in their own right.
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PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.

TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
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Exploring complex networks

TL;DR: This work aims to understand how an enormous network of interacting dynamical systems — be they neurons, power stations or lasers — will behave collectively, given their individual dynamics and coupling architecture.
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Mosaic organization of DNA nucleotides

TL;DR: This work analyzes two classes of controls consisting of patchy nucleotide sequences generated by different algorithms--one without and one with long-range power-law correlations, finding that both types of sequences are quantitatively distinguishable by an alternative fluctuation analysis method.
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Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series

TL;DR: 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.
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