Open AccessEpileptic EEG information entropy based on different entropy estimation methods
Author(s) -
Min Li,
Yunjie Fang,
Yu Zhang,
Shujun Zhang,
Junwei Chen,
Bingxin Zhu,
Wei Yan,
Jun Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1592/1/012039
Subject(s) - electroencephalography , entropy (arrow of time) , pattern recognition (psychology) , entropy estimation , artificial intelligence , sample entropy , maximum entropy spectral estimation , principle of maximum entropy , kernel density estimation , nonlinear system , mathematics , computer science , approximate entropy , statistics , neuroscience , psychology , physics , quantum mechanics , estimator
We use the information entropy based on linear model, K-nearest neighbour estimation and kernel estimation to study the brain, a complex nonlinear dynamic system, and distinguish the nonlinear dynamic complexity of epileptic and normal EEG signals in Bonn database. The entropy estimation method of linear model and K-nearest neighbour estimation can only distinguish the information entropy of epileptic period from that of other two cases, but it can’t distinguish the information entropy of epileptic interval and normal EEG signals. However, kernel estimation can distinguish the information entropy of EEG signals in three states well, and the threshold range is [0.1,3]. With the increase of threshold, the discrimination effect is gradually significant until stable, and the discrimination effect is obvious when the threshold is 0.5. The result of analysis indicated that EEG information entropy was the highest in the epileptic seizure period, followed by the epileptic seizure interval, and the lowest in normal human brain.