Journal
EPILEPSY RESEARCH
Volume 104, Issue 3, Pages 246-252Publisher
ELSEVIER
DOI: 10.1016/j.eplepsyres.2012.11.003
Keywords
Absence seizure; EEG; Multiscale permutation entropy; Classification
Categories
Funding
- National Natural Science Foundation of China [61105027, 61025019]
- China Post-doctoral Science Foundation [20100470991, 201104391]
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Understanding the transition of brain activities towards an absence seizure, called pre-epileptic seizure, is a challenge. In this study, multiscale permutation entropy (MPE) is proposed to describe dynamical characteristics of electroencephalograph (EEG) recordings on different absence seizure states. The classification ability of the MPE measures using linear discriminant analysis is evaluated by a series of experiments. Compared to a traditional multiscale entropy method with 86.1% as its classification accuracy, the classification rate of MPE is 90.6%. Experimental results demonstrate there is a reduction of permutation entropy of EEG from the seizure-free state to the seizure state. Moreover, it is indicated that the dynamical characteristics of EEG data with MPE can identify the differences among seizure-free, pre-seizure and seizure states. This also supports the view that EEG has a detectable change prior to an absence seizure. (C) 2012 Elsevier B.V. All rights reserved.
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