Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures
Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures
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使用排列熵测量失神发作期间脑电图信号的变化
DOI：
10.3390/e16063049
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发表时间：
2014-06-01
期刊：
影响因子：
2.7
通讯作者：
Ouyang, Gaoxiang
中科院分区：
文献类型：
--
作者：
Li, Jing;Yan, Jiaqing;Ouyang, Gaoxiang
在本文中，我们提出使用排列熵来探索脑电图（EEG）数据的变化是否可以有效地区分人类失神癫痫的不同阶段，即，无癫痫发作、癫痫发作前和癫痫发作阶段。应用排列熵来分析这三个阶段的EEG数据，每个阶段包含100个持续时间为2 s的19通道EEG历元。实验结果表明，PE的平均值从无癫痫发作期到癫痫发作期逐渐降低，为有效区分失神癫痫的三个不同发作期提供了依据。此外，我们的研究结果加强了这样一种观点，即大多数额叶电极携带有用的信息和模式，可以帮助区分不同的失神发作阶段。
In this paper, we propose to use permutation entropy to explore whether the changes in electroencephalogram (EEG) data can effectively distinguish different phases in human absence epilepsy, i.e., the seizure-free, the pre-seizure and seizure phases. Permutation entropy is applied to analyze the EEG data from these three phases, each containing 100 19-channel EEG epochs of 2 s duration. The experimental results show the mean value of PE gradually decreases from the seizure-free to the seizure phase and provides evidence that these three different seizure phases in absence epilepsy can be effectively distinguished. Furthermore, our results strengthen the view that most frontal electrodes carry useful information and patterns that can help discriminate among different absence seizure phases.