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Detecting dynamical changes in time series using the permutation entropy.

Yinhe Cao1, Wen-Wen Tung, J B Gao

  • 1BioSieve, San Jose, California 95117, USA. contact@biosieve.com

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 17, 2004
PubMed
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Permutation entropy offers a simple yet effective method for detecting changes in complex systems. This approach successfully identified dynamical shifts in model systems and epileptic brain wave data.

Area of Science:

  • Complex systems analysis
  • Nonlinear dynamics
  • Biomedical signal processing

Background:

  • Detecting unusual events in natural and man-made systems is crucial.
  • Dynamical changes require robust detection methods.

Purpose of the Study:

  • To evaluate permutation entropy as a tool for detecting dynamical changes.
  • To demonstrate its applicability across different types of systems.

Main Methods:

  • Utilized permutation entropy, a recently proposed measure.
  • Applied the method to two distinct model systems.
  • Analyzed clinically characterized brain wave data from epileptic patients.

Main Results:

  • Permutation entropy effectively detected qualitative and quantitative dynamical changes.

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  • The measure proved useful in identifying anomalies in both model systems and real-world biological data.
  • Successful application to epileptic seizure detection in brain waves.

    • Conclusions:

    • Permutation entropy is a versatile and effective measure for anomaly detection.
    • Its simplicity and ease of calculation make it broadly applicable.
    • Demonstrated potential for real-time event detection in critical systems.