Coarse-grained entropy rates for characterization of complex time series

Details Coarse-grained entropy rates for characterization of complex time series Coarse-grained entropy rates for characterization of complex time series

1995-12-13

arxiv.org/abs/comp-gas/9512002v1

Nonlinear Sciences

Cellular Automata and Lattice Gases

15 pages, uuencoded compressed postscript file, figures embedded in the text, <mp@uivt.cas.cz>, <mp@santafe.edu>

Scientific paper

A method for classification of complex time series using coarse-grained entropy rates (CER's) is presented. The CER's, which are computed from information-theoretic functionals -- redundancies, are relative measures of regularity and predictability, and for data generated by dynamical systems they are related to Kolmogorov-Sinai entropy. A deterministic dynamical origin of the data under study, however, is not a necessary condition for the use of the CER's, since the entropy rates can be defined for stochastic processes as well. Sensitivity of the CER's to changes in data dynamics and their robustness with respect to noise are tested by using numerically generated time series resulted from both deterministic -- chaotic and stochastic processes. Potential application of the CER's in analysis of physiological signals or other complex time series is demonstrated by using examples from pharmaco-EEG and tremor classification.

Affiliated with

Also associated with

No associations

Rating

Coarse-grained entropy rates for characterization of complex time series does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Coarse-grained entropy rates for characterization of complex time series, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Coarse-grained entropy rates for characterization of complex time series will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-615698