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      Approximate entropy as a measure of system complexity.

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          Abstract

          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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          Author and article information

          Journal
          Proc Natl Acad Sci U S A
          Proceedings of the National Academy of Sciences of the United States of America
          Proceedings of the National Academy of Sciences
          0027-8424
          0027-8424
          Mar 15 1991
          : 88
          : 6
          Affiliations
          [1 ] 990 Moose Hill Road, Guilford, CT 06437, USA.
          Article
          10.1073/pnas.88.6.2297
          51218
          11607165
          09106140-f88a-4e32-b593-e18095db58d0
          History

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