Using the Memories of Multiscale Machines to Characterize Complex Systems

Nick S. Jones
Phys. Rev. Lett. 100, 208702 – Published 22 May 2008

Abstract

A scheme is presented to extract detailed dynamical signatures from successive measurements of complex systems. Relative entropy based time series tools are used to quantify the gain in predictive power of increasing past knowledge. By lossy compression, data is represented by increasingly coarsened symbolic strings. Each compression resolution is modeled by a machine: a finite memory transition matrix. Applying the relative entropy tools to each machine’s memory exposes correlations within many time scales. Examples are given for cardiac arrhythmias and different heart conditions are distinguished.

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  • Received 8 April 2007

DOI:https://doi.org/10.1103/PhysRevLett.100.208702

©2008 American Physical Society

Authors & Affiliations

Nick S. Jones

  • Oxford Centre for Integrative Systems Biology, Departments of Physics and Biochemistry, Oxford University, Oxford OX1 3PU, United Kingdom

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Vol. 100, Iss. 20 — 23 May 2008

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