Permutation Entropy: Too Complex a Measure for EEG Time Series?
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 ch...
| Main Authors: | , , , |
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| Format: | Article |
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MDPI AG
2017-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/19/12/692 |
| _version_ | 1828394898470993920 |
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| author | Sebastian Berger Gerhard Schneider Eberhard F. Kochs Denis Jordan |
| author_facet | Sebastian Berger Gerhard Schneider Eberhard F. Kochs Denis Jordan |
| author_sort | Sebastian Berger |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T08:03:40Z |
| format | Article |
| id | doaj.art-effdc206dfe84cb6ac28236391c1debb |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-12-10T08:03:40Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-effdc206dfe84cb6ac28236391c1debb2022-12-22T01:56:44ZengMDPI AGEntropy1099-43002017-12-01191269210.3390/e19120692e19120692Permutation Entropy: Too Complex a Measure for EEG Time Series?Sebastian Berger0Gerhard Schneider1Eberhard F. Kochs2Denis Jordan3Department of Anaesthesiology, Klinikum rechts der Isar der Technischen Universität München (MRI TUM), 81675 Munich, GermanyDepartment of Anaesthesiology, Klinikum rechts der Isar der Technischen Universität München (MRI TUM), 81675 Munich, GermanyDepartment of Anaesthesiology, Klinikum rechts der Isar der Technischen Universität München (MRI TUM), 81675 Munich, GermanyInstitute of Geomatics Engineering, University of Applied Sciences and Arts Northwestern Switzerland, 4132 Muttenz, SwitzerlandPermutation 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.https://www.mdpi.com/1099-4300/19/12/692permutation entropyordinal pattern analysiselectroencephalography |
| spellingShingle | Sebastian Berger Gerhard Schneider Eberhard F. Kochs Denis Jordan Permutation Entropy: Too Complex a Measure for EEG Time Series? Entropy permutation entropy ordinal pattern analysis electroencephalography |
| title | Permutation Entropy: Too Complex a Measure for EEG Time Series? |
| title_full | Permutation Entropy: Too Complex a Measure for EEG Time Series? |
| title_fullStr | Permutation Entropy: Too Complex a Measure for EEG Time Series? |
| title_full_unstemmed | Permutation Entropy: Too Complex a Measure for EEG Time Series? |
| title_short | Permutation Entropy: Too Complex a Measure for EEG Time Series? |
| title_sort | permutation entropy too complex a measure for eeg time series |
| topic | permutation entropy ordinal pattern analysis electroencephalography |
| url | https://www.mdpi.com/1099-4300/19/12/692 |
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