Predictability analysis of absence seizures with permutation entropy.

In this study, we investigate permutation entropy as a tool to predict the absence seizures of genetic absence epilepsy rats from Strasbourg (GAERS) by using EEG recordings. The results show that permutation entropy can track the dynamical changes of EEG data, so as to describe transient dynamics prior to the absence seizures. Experiments demonstrate that permutation entropy can successfully detect pre-seizure state in 169 out of 314 seizures from 28 rats and the average anticipation time of permutation entropy is around 4.9s. These findings could shed new light on the mechanism of absence seizure. In comparison with results of sample entropy, permutation entropy is better able to predict absence seizures.