Dept. of Neurology, University of Michigan, USA.
Epilepsy Res. 2011 Dec;97(3):243-51. doi: 10.1016/j.eplepsyres.2011.07.012. Epub 2011 Aug 31.
EEG-based seizure prediction has undergone phases of optimism when analyses based on limited EEG samples suggested high sensitivity and specificity for several algorithms extracting features from raw preictal EEG data. When using long-term recordings, a more realistic view emerged which suggests that statistically significant predictions might be possible from surface and intracranial EEG, but no algorithm has yet demonstrated performance allowing for clinical application. Here, progress in EEG recording techniques, EEG analysis, and requirements for proper statistical validation of results are reported and discussed as they pertain to clinical implementation.
基于脑电图的癫痫发作预测经历了几个阶段,当基于有限的脑电图样本的分析表明,从原始的癫痫发作前脑电图数据中提取特征的几种算法具有很高的敏感性和特异性时,人们对此感到乐观。当使用长期记录时,出现了一个更现实的观点,即从表面和颅内脑电图中可能可以进行具有统计学意义的预测,但目前还没有一种算法能够表现出允许临床应用的性能。本文报告并讨论了与临床应用相关的脑电图记录技术、脑电图分析以及对结果进行适当的统计验证的要求方面的进展。