Next Article in Journal
Stochastic Thermodynamics: A Dynamical Systems Approach
Next Article in Special Issue
Characterizing Complex Dynamics in the Classical and Semi-Classical Duffing Oscillator Using Ordinal Patterns Analysis
Previous Article in Journal
Non-Equilibrium Thermodynamic Analysis of Double Diffusive, Nanofluid Forced Convection in Catalytic Microreactors with Radiation Effects
Previous Article in Special Issue
Random Walk Null Models for Time Series Data
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Entropy 2017, 19(12), 692; https://doi.org/10.3390/e19120692

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.
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)
Full-Text   |   PDF [1364 KB, uploaded 16 December 2017]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Berger, S.; Schneider, G.; Kochs, E.F.; Jordan, D. Permutation Entropy: Too Complex a Measure for EEG Time Series? Entropy 2017, 19, 692.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top