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Open AccessArticle

Permutation Entropy: Too Complex a Measure for EEG Time Series?

1
Department of Anaesthesiology, Klinikum rechts der Isar der Technischen Universität München (MRI TUM), 81675 Munich, Germany
2
Institute of Geomatics Engineering, University of Applied Sciences and Arts Northwestern Switzerland, 4132 Muttenz, Switzerland
*
Author to whom correspondence should be addressed.
Entropy 2017, 19(12), 692; https://doi.org/10.3390/e19120692
Received: 16 November 2017 / Revised: 11 December 2017 / Accepted: 13 December 2017 / Published: 16 December 2017
(This article belongs to the Special Issue Permutation Entropy & Its Interdisciplinary Applications)
Permutation entropy (PeEn) is a complexity measure that originated from dynamical systems theory. Specifically engineered to be robustly applicable to real-world data, the quantity has since been utilised for a multitude of time series analysis tasks. In electroencephalogram (EEG) analysis, value changes of PeEn correlate with clinical observations, among them the onset of epileptic seizures or the loss of consciousness induced by anaesthetic agents. Regarding this field of application, the present work suggests a relation between PeEn-based complexity estimation and spectral methods of EEG analysis: for ordinal patterns of three consecutive samples, the PeEn of an epoch of EEG appears to approximate the centroid of its weighted power spectrum. To substantiate this proposition, a systematic approach based on redundancy reduction is introduced and applied to sleep and epileptic seizure EEG. The interrelation demonstrated may aid the interpretation of PeEn in EEG, and may increase its comparability with other techniques of EEG analysis. View Full-Text
Keywords: permutation entropy; ordinal pattern analysis; electroencephalography permutation entropy; ordinal pattern analysis; electroencephalography
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Berger, S.; Schneider, G.; Kochs, E.F.; Jordan, D. Permutation Entropy: Too Complex a Measure for EEG Time Series? Entropy 2017, 19, 692.

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