Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic
Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic
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DOI：
10.1016/j.physd.2004.02.013
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发表时间：
2004-07-15
影响因子：
4
通讯作者：
Timmer, J
中科院分区：
文献类型：
--
作者：
Maiwald, T;Winterhalder, M;Timmer, J
癫痫的特征是自发和不可预见的癫痫发作，在此期间患者的感知或行为受到干扰。这些癫痫发作的可预测性将使新的治疗方法成为可能。几种预测方法声称能够提前几分钟根据EEG记录预测癫痫发作。然而，术语癫痫发作预测没有明确的定义，存在不同的标准来评估预测方法，并且很少关注敏感性和错误预测率的问题。我们引入了一个评估标准，称为癫痫发作预测特征，它包括敏感性和错误预测率的评估。在此框架内，三个非线性癫痫发作预测方法进行了评估，一个大的EEG数据池的21名患者。总共检查了582次颅内EEG数据和88次癫痫发作。在每天1-3.6次错误预测的情况下，“动态相似性指数”的灵敏度在21%到42%之间，这是三种方法中最好的结果。扩展的，前瞻性的版本的“积累的能量”和“有效的相关维度”的灵敏度在18和31%之间，在13和30%之间。这些结果仍然不足以用于临床应用。(C)2004 Elsevier B. V.保留所有权利。
Epilepsy is characterized by the spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. The predictability of these seizures would render novel therapeutic approaches possible. Several prediction methods have claimed to be able to predict seizures based on EEG recordings minutes in advance. However, the term seizure prediction is not unequivocally defined, different criteria to assess prediction methods exist, and only little attention has been paid to issues of sensitivity and false prediction rate. We introduce an assessment criterion called the seizure prediction characteristic that incorporates the assessment of sensitivity and false prediction rate. Within this framework, three nonlinear seizure prediction methods were evaluated on a large EEG data pool of 21 patients. Altogether, 582 It intracranial EEG data and 88 seizures were examined. With a rate of 1-3.6 false predictions per day, the "dynamical similarity index" achieves a sensitivity between 21 and 42%, which was the best result of the three methods. Sensitivity was between 18 and 31% for the extended, prospective version of the "accumulated energy" and between 13 and 30% for the "effective correlation dimension". These results still are not sufficient for clinical applications. (C) 2004 Elsevier B.V. All rights reserved.