Andrzejak Ralph G, Mormann Florian, Kreuz Thomas, Rieke Christoph, Kraskov Alexander, Elger Christian E, Lehnertz Klaus
John-von-Neumann Institute for Computing, Forschungszentrum Jülich, 52425 Jülich, Germany.
Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Jan;67(1 Pt 1):010901. doi: 10.1103/PhysRevE.67.010901. Epub 2003 Jan 7.
A rapidly growing number of studies deals with the prediction of epileptic seizures. For this purpose, various techniques derived from linear and nonlinear time series analysis have been applied to the electroencephalogram of epilepsy patients. In none of these works, however, the performance of the seizure prediction statistics is tested against a null hypothesis, an otherwise ubiquitous concept in science. In consequence, the evaluation of the reported performance values is problematic. Here, we propose the technique of seizure time surrogates based on a Monte Carlo simulation to remedy this deficit.
越来越多的研究致力于癫痫发作的预测。为此，各种源自线性和非线性时间序列分析的技术已被应用于癫痫患者的脑电图。然而，在这些研究中，没有一项将癫痫发作预测统计的性能与零假设进行检验，而零假设在科学中是一个普遍存在的概念。因此，对所报告的性能值进行评估存在问题。在此，我们提出基于蒙特卡罗模拟的癫痫发作时间替代技术来弥补这一不足。
