Li Xiaoli, Ouyang Gaoxian, Richards Douglas A
Cercia, School of Computer Science, The University of Birmingham, Birmingham B15 2TT, UK.
Epilepsy Res. 2007 Oct;77(1):70-4. doi: 10.1016/j.eplepsyres.2007.08.002. Epub 2007 Sep 17.
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.
在本研究中，我们通过脑电图记录，将排列熵作为一种工具来预测来自斯特拉斯堡的遗传性失神癫痫大鼠（GAERS）的失神发作。结果表明，排列熵可以追踪脑电图数据的动态变化，从而描述失神发作前的瞬态动力学。实验表明，排列熵能够成功检测出28只大鼠314次发作中的169次发作前状态，排列熵的平均预测时间约为4.9秒。这些发现可能为失神发作的机制提供新的线索。与样本熵的结果相比，排列熵更能预测失神发作。
