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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

Summary

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.

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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.