Detecting dynamical changes in time series using the permutation entropy

Yinhe Cao, Wen-wen Tung, J. B. Gao, V. A. Protopopescu, and L. M. Hively

Phys. Rev. E 70, 046217 – Published 27 October 2004

Abstract

Timely detection of unusual and/or unexpected events in natural and man-made systems has deep scientific and practical relevance. We show that the recently proposed conceptually simple and easily calculated measure of permutation entropy can be effectively used to detect qualitative and quantitative dynamical changes. We illustrate our results on two model systems as well as on clinically characterized brain wave data from epileptic patients.

Received 5 April 2004

DOI:https://doi.org/10.1103/PhysRevE.70.046217

Authors & Affiliations

Yinhe Cao^1,3,*, Wen-wen Tung^2,†, J. B. Gao^3,‡, V. A. Protopopescu^4,§, and L. M. Hively^4,∥

¹BioSieve, San Jose, California 95117, USA
²National Center for Atmospheric Research, P.O. Box 3000, Boulder, Colorado 80307-3000, USA
³Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida 32611, USA
⁴Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831-6418, USA

^*Electronic address: contact@biosieve.com
^†Author to whom correspondence should be addressed. Electronic address: wwtung@ucar.edu
^‡Electronic address: gao@ece.ufl.edu
^§Electronic address: protopopesva@ornl.gov
^∥Electronic address: hivelylm@ornl.gov

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Issue

Vol. 70, Iss. 4 — October 2004

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