AI Article Synopsis
- The study focuses on understanding the brain activity changes that occur before absence seizures, known as pre-epileptic seizures.
- MPE (multiscale permutation entropy) is introduced as a new method for analyzing EEG recordings, showing better classification accuracy (90.6%) compared to traditional methods (86.1%).
- Results indicate that EEG data shows detectable changes in dynamical characteristics as it transitions from a seizure-free state to a pre-seizure and then to a seizure state.
Article Abstract
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.
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| http://dx.doi.org/10.1016/j.eplepsyres.2012.11.003 | DOI Listing |
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