Characterizing dynamics of absence seizure EEG with spatial-temporal permutation entropy
Characterizing dynamics of absence seizure EEG with spatial-temporal permutation entropy
复制标题
用时空排列熵表征失神发作脑电图的动力学
DOI：
10.1016/j.neucom.2017.09.007
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发表时间：
2018-01
期刊：
影响因子：
6
通讯作者：
Li Xiao-Li
中科院分区：
文献类型：
--
作者：
Zeng Ke;Ou Yang Gao-Xiang;Chen He;Gu Yue;Liu Xian-Zeng;Li Xiao-Li
癫痫发作前短暂脑动力学的表征是失神癫痫研究的主要挑战。由于大脑是一个混沌动力学系统，许多基于复杂性的方法被用来跟踪失神发作脑电的动态变化。然而，这些方法大多将多通道EEG记录视为一组独立的时间序列，这将不可避免地导致癫痫网络中关键的跨通道相关性的丢失。近年来，提出了一种时空排列熵的多尺度排列熵（MMPE）方法来度量多通道数据的复杂性。在这项研究中，MMPE被应用于多通道脑电图的失神发作的动力学特征。结果发现，发作前脑电MMPE值显著低于发作间期，而MMPE值显著高于发作期，说明多通道脑电的复杂性在脑活动的转换过程中降低。这一发现证实了失神癫痫发作前状态的存在。MMPE的识别能力进行了测试，其原始的单变量复杂性措施：排列熵（PE）和多尺度排列熵（MSPE），和另一个多变量多尺度熵：多变量多尺度样本熵（MMSE）。通过四种分类器（决策树、K近邻、判别分析、支持向量机）的性能评估，MMPE的分类准确率至少达到87.2%，比PE、MSPE和MMSE分别提高了15%、12%和10%。因此，这项工作支持的观点，即脑电图有一个可检测的变化之前，失神发作，MMPE可以被认为是一个候选的前兆即将到来的失神发作。
Characterizing transient brain dynamics prior to seizures is a main challenge in absence epilepsy study. As brain is a chaos dynamical system, many complexity based methods have been used to track the dynamical changes of absence seizure EEG. However, most of these methods treat multichannel EEG recordings as a set of individual time series, which will inevitably lead to the loss of crucial cross-channel correlation in the epileptic network. Recently, a spatial-temporal permutation entropy method called multivariate multiscale permutation entropy (MMPE) was proposed to measure the complexity of multichannel data. In this study, MMPE was applied to multichannel EEG for characterizing dynamics of absence seizure. It was found that the pre-ictal EEG exhibited a significant lower MMPE value than interictal EEG, and a significant higher MMPE value than the ictal EEG, indicating that the complexity of multichannel EEG decreased in the transition of brain activities. This finding confirmed the existence of a pre-seizure state in absence epilepsy. The identification ability of MMPE was tested against its original univariate complexity measures: permutation entropy (PE) and multiscale permutation entropy (MSPE), and another multivariate multiscale entropy: multivariate multiscale sample entropy (MMSE). After evaluating the performance by four classifiers (Decision Tree,K-Nearest Neighbor, Discriminant Analysis, Support Vector Machine), MMPE can achieve accuracy of 87.2% at least, which is about 15%, 12%, and 10% higher than that of PE, MSPE and MMSE. Hence, this work supports the view that EEG has a detectable change prior to an absence seizure, and MMPE could be considered as a candidate precursor of the impending absence seizures.
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DOI：
10.2307/2982750
发表时间：
1992-03
期刊：
--
影响因子：
--
作者：
G. McLachlan
通讯作者：
G. McLachlan
使用排列熵测量失神发作期间脑电图信号的变化
影响因子：
2.7
作者：
Li, Jing;Yan, Jiaqing;Ouyang, Gaoxiang
通讯作者：
Ouyang, Gaoxiang
多尺度排列熵分析失神脑电图记录的动态特征
影响因子：
2.2
作者：
Ouyang, Gaoxiang;Li, Jing;Li, Xiaoli
通讯作者：
Li, Xiaoli
影响因子：
3.2
作者：
Liu, Chengyu;Liu, Changchun;Liu, Feng
通讯作者：
Liu, Feng
影响因子：
2.7
作者：
Morabito, Francesco Carlo;Labate, Domenico;Palamara, Isabella
通讯作者：
Palamara, Isabella