Permutation Entropy: Too Complex a Measure for EEG Time Series?
AbstractPermutation 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
- Supplementary File 1:
ZIP-Document (ZIP, 21 KB)
Share & Cite This Article
Berger, S.; Schneider, G.; Kochs, E.F.; Jordan, D. Permutation Entropy: Too Complex a Measure for EEG Time Series? Entropy 2017, 19, 692.
Berger S, Schneider G, Kochs EF, Jordan D. Permutation Entropy: Too Complex a Measure for EEG Time Series? Entropy. 2017; 19(12):692.Chicago/Turabian Style
Berger, Sebastian; Schneider, Gerhard; Kochs, Eberhard F.; Jordan, Denis. 2017. "Permutation Entropy: Too Complex a Measure for EEG Time Series?" Entropy 19, no. 12: 692.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.