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      Physiological time-series analysis using approximate entropy and sample entropy

      1 , 2 , 1

      American Journal of Physiology-Heart and Circulatory Physiology

      American Physiological Society

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          Abstract

          Entropy, as it relates to dynamical systems, is the rate of information production. Methods for estimation of the entropy of a system represented by a time series are not, however, well suited to analysis of the short and noisy data sets encountered in cardiovascular and other biological studies. Pincus introduced approximate entropy (ApEn), a set of measures of system complexity closely related to entropy, which is easily applied to clinical cardiovascular and other time series. ApEn statistics, however, lead to inconsistent results. We have developed a new and related complexity measure, sample entropy (SampEn), and have compared ApEn and SampEn by using them to analyze sets of random numbers with known probabilistic character. We have also evaluated cross-ApEn and cross-SampEn, which use cardiovascular data sets to measure the similarity of two distinct time series. SampEn agreed with theory much more closely than ApEn over a broad range of conditions. The improved accuracy of SampEn statistics should make them useful in the study of experimental clinical cardiovascular and other biological time series.

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          Most cited references 25

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          Ergodic theory of chaos and strange attractors

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            Approximate entropy as a measure of system complexity.

             S Pincus (1991)
            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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              • Record: found
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              Estimation of the Kolmogorov entropy from a chaotic signal

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                Author and article information

                Journal
                American Journal of Physiology-Heart and Circulatory Physiology
                American Journal of Physiology-Heart and Circulatory Physiology
                American Physiological Society
                0363-6135
                1522-1539
                June 2000
                June 2000
                : 278
                : 6
                : H2039-H2049
                Affiliations
                [1 ]Cardiovascular Division, Department of Internal Medicine, and Department of Molecular Physiology and Biological Physics, and Cardiovascular Research Center, University of Virginia Health Sciences Center, Charlottesville, Virginia 22908; and
                [2 ]Medical Automation Systems, Charlottesville, Virginia 22903
                Article
                10.1152/ajpheart.2000.278.6.H2039
                10843903
                © 2000

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