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Entropy
Volume 22
Issue 10
10.3390/e22101092
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Article

A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns

by
Xian Liu
* and
Zhuang Fu
Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Yanshan University, Qinhuangdao 066004, China
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(10), 1092; https://doi.org/10.3390/e22101092
Submission received: 15 August 2020 / Revised: 23 September 2020 / Accepted: 24 September 2020 / Published: 29 September 2020
(This article belongs to the Special Issue Entropy in Brain Networks)
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Abstract

Epilepsy is one of the most ordinary neuropathic illnesses, and electroencephalogram (EEG) is the essential method for recording various brain rhythm activities due to its high temporal resolution. The conditional entropy of ordinal patterns (CEOP) is known to be fast and easy to implement, which can effectively measure the irregularity of the physiological signals. The present work aims to apply the CEOP to analyze the complexity characteristics of the EEG signals and recognize the epilepsy EEG signals. We discuss the parameter selection and the performance analysis of the CEOP based on the neural mass model. The CEOP is applied to the real EEG database of Bonn epilepsy for identification. The results show that the CEOP is an excellent metrics for the analysis and recognition of epileptic EEG signals. The differences of the CEOP in normal and epileptic brain states suggest that the CEOP could be a judgment tool for the diagnosis of the epileptic seizure.
Keywords: epileptic seizure; electroencephalogram (EEG); conditional entropy of ordinal patterns (CEOP); neural mass model; recognition epileptic seizure; electroencephalogram (EEG); conditional entropy of ordinal patterns (CEOP); neural mass model; recognition

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MDPI and ACS Style

Liu, X.; Fu, Z. A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns. Entropy 2020, 22, 1092. https://doi.org/10.3390/e22101092

AMA Style

Liu X, Fu Z. A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns. Entropy. 2020; 22(10):1092. https://doi.org/10.3390/e22101092

Chicago/Turabian Style

Liu, Xian, and Zhuang Fu. 2020. "A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns" Entropy 22, no. 10: 1092. https://doi.org/10.3390/e22101092

APA Style

Liu, X., & Fu, Z. (2020). A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns. Entropy, 22(10), 1092. https://doi.org/10.3390/e22101092

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