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Entropy 2014, 16(12), 6212-6239; doi:10.3390/e16126212

Ordinal Patterns, Entropy, and EEG

1
Institute of Mathematics, University of Lübeck, Lübeck D-23562, Germany
2
Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, Lübeck D-23562, Germany
*
Author to whom correspondence should be addressed.
Received: 9 October 2014 / Revised: 13 November 2014 / Accepted: 19 November 2014 / Published: 27 November 2014
(This article belongs to the Special Issue Entropy and Electroencephalography)
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Abstract

In this paper we illustrate the potential of ordinal-patterns-based methods for analysis of real-world data and, especially, of electroencephalogram (EEG) data. We apply already known (empirical permutation entropy, ordinal pattern distributions) and new (empirical conditional entropy of ordinal patterns, robust to noise empirical permutation entropy) methods for measuring complexity, segmentation and classification of time series. View Full-Text
Keywords: ordinal patterns; permutation entropy; approximate entropy; sample entropy; conditional entropy of ordinal patterns; segmentation; clustering; change-points detection ordinal patterns; permutation entropy; approximate entropy; sample entropy; conditional entropy of ordinal patterns; segmentation; clustering; change-points detection
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).

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Keller, K.; Unakafov, A.M.; Unakafova, V.A. Ordinal Patterns, Entropy, and EEG. Entropy 2014, 16, 6212-6239.

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