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      Complex systems and the technology of variability analysis

      research-article
      1 , , 2
      Critical Care
      BioMed Central
      complex systems, critical illness, entropy, therapeutic monitoring, variability

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          Abstract

          Characteristic patterns of variation over time, namely rhythms, represent a defining feature of complex systems, one that is synonymous with life. Despite the intrinsic dynamic, interdependent and nonlinear relationships of their parts, complex biological systems exhibit robust systemic stability. Applied to critical care, it is the systemic properties of the host response to a physiological insult that manifest as health or illness and determine outcome in our patients. Variability analysis provides a novel technology with which to evaluate the overall properties of a complex system. This review highlights the means by which we scientifically measure variation, including analyses of overall variation (time domain analysis, frequency distribution, spectral power), frequency contribution (spectral analysis), scale invariant (fractal) behaviour (detrended fluctuation and power law analysis) and regularity (approximate and multiscale entropy). Each technique is presented with a definition, interpretation, clinical application, advantages, limitations and summary of its calculation. The ubiquitous association between altered variability and illness is highlighted, followed by an analysis of how variability analysis may significantly improve prognostication of severity of illness and guide therapeutic intervention in critically ill patients.

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          Most cited references150

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          Approximate entropy as a measure of system complexity.

          Techniques to determine changing system complexity from data are evaluated. Convergence of a frequently used correlation dimension algorithm to a finite value does not necessarily imply an underlying deterministic model or chaos. Analysis of a recently developed family of formulas and statistics, approximate entropy (ApEn), suggests that ApEn can classify complex systems, given at least 1000 data values in diverse settings that include both deterministic chaotic and stochastic processes. The capability to discern changing complexity from such a relatively small amount of data holds promise for applications of ApEn in a variety of contexts.
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            Multiscale entropy analysis of complex physiologic time series.

            There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.
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              Scaling the microrheology of living cells.

              We report a scaling law that governs both the elastic and frictional properties of a wide variety of living cell types, over a wide range of time scales and under a variety of biological interventions. This scaling identifies these cells as soft glassy materials existing close to a glass transition, and implies that cytoskeletal proteins may regulate cell mechanical properties mainly by modulating the effective noise temperature of the matrix. The practical implications are that the effective noise temperature is an easily quantified measure of the ability of the cytoskeleton to deform, flow, and reorganize.
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                Author and article information

                Journal
                Crit Care
                Critical Care
                BioMed Central (London )
                1364-8535
                1466-609X
                2004
                22 September 2004
                : 8
                : 6
                : R367-R384
                Affiliations
                [1 ]Assistant Professor, Thoracic Surgery and Critical Care Medicine, University of Ottawa, Ottawa, Ontario, Canada
                [2 ]Professor Emeritus, Respiratory Medicine, McGill University, Montreal, Quebec, Canada
                Article
                cc2948
                10.1186/cc2948
                1065053
                15566580
                26dce060-dc2b-4305-91d9-26e578209fab
                Copyright © 2004 Seely et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 August 2004
                : 7 July 2004
                : 5 August 2004
                : 9 August 2004
                Categories
                Research

                Emergency medicine & Trauma
                critical illness,entropy,therapeutic monitoring,variability,complex systems

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