Performance of a seizure warning algorithm based on the dynamics of intracranial EEG
Performance of a seizure warning algorithm based on the dynamics of intracranial EEG
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DOI：
10.1016/j.eplepsyres.2005.03.009
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发表时间：
2005-05-01
影响因子：
2.2
通讯作者：
Sackellares, JC
中科院分区：
文献类型：
--
作者：
Chaovalitwongse, W;Lasemidis, LD;Sackellares, JC
在过去的十年中，一些研究已经证明，在颞叶癫痫发作之前，脑电图仪(EEG)信号的动力学特性(空间和时间)都会发生变化。在这项研究中，我们评估了一种基于混沌理论和全局优化技术的方法，通过监测脑电信号动力学的时空变化来检测癫痫发作前的状态。该方法使用短期最大Lyapunov指数(STLmax)的估计来量化每个电极位置的脑电动力学。还采用了一种全局优化技术来确定与癫痫发作发展有关的关键电极位置。这项研究的一个重要实用成果是开发了一个自动癫痫发作警报系统(ASWS)。该算法在10例难治性颞叶癫痫患者的连续、长期的脑电记录中进行了测试，持续时间为3-14天。在这项分析中，对于每个患者，脑电记录被分为训练和测试数据集。我们使用包含一半癫痫发作的第一部分数据来训练该算法，该算法的灵敏度为76.12%，总体错误预测率为0.17h(-1)。在训练阶段得到的最优参数设置下，该算法在测试阶段的预测性能达到了68.75%的灵敏度，总的误预测率为0.15h(-1)。这项研究的结果证实了我们之前从少数患者那里观察到的结论：开发用于诊断和治疗目的的自动癫痫警报设备是可行的和实用的。(C)2005 Elsevier B.V.保留所有权利。
During the past decade, several studies have demonstrated experimental evidence that temporal lobe seizures are preceded by changes in dynamical properties (both spatial and temporal) of electroencephalograph (EEG) signals. In this study, we evaluate a method, based on chaos theory and global optimization techniques, for detecting pre-seizure states by monitoring the spatio-temporal changes in the dynamics of the EEG signal. The method employs the estimation of the short-term maximum Lyapunov exponent (STLmax), a measure of the order (chaoticity) of a dynamical system, to quantify the EEG dynamics per electrode site. A global optimization technique is also employed to identify critical electrode sites that are involved in the seizure development. An important practical result of this study was the development of an automated seizure warning system (ASWS).The algorithm was tested in continuous, long-term EEG recordings, 3-14 days in duration, obtained from 10 patients with refractory temporal lobe epilepsy. In this analysis, for each patient, the EEG recordings were divided into training and testing datasets. We used the first portion of the data that contained half of the seizures to train the algorithm, where the algorithm achieved a sensitivity of 76.12% with an overall false prediction rate of 0.17 h(-1). With the optimal parameter setting obtained from the training phase, the prediction performance of the algorithm during the testing phase achieved a sensitivity of 68.75% with an overall false prediction rate of 0.15 h(-1). The results of this study confirm our previous observations from a smaller number of patients: the development of automated seizure warning devices for diagnostic and therapeutic purposes is feasible and practically useful. (C) 2005 Elsevier B.V. All rights reserved.