Andrzejak Ralph G, David Olivier, Gnatkovsky Vadym, Wendling Fabrice, Bartolomei Fabrice, Francione Stefano, Kahane Philippe, Schindler Kaspar, de Curtis Marco
Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Brain Function and Neuromodulation, Université Joseph Fourier, Grenoble, France.
Brain Topogr. 2015 Nov;28(6):832-7. doi: 10.1007/s10548-014-0380-8. Epub 2014 Jun 15.
In patients diagnosed with pharmaco-resistant epilepsy, cerebral areas responsible for seizure generation can be defined by performing implantation of intracranial electrodes. The identification of the epileptogenic zone (EZ) is based on visual inspection of the intracranial electroencephalogram (IEEG) performed by highly qualified neurophysiologists. New computer-based quantitative EEG analyses have been developed in collaboration with the signal analysis community to expedite EZ detection. The aim of the present report is to compare different signal analysis approaches developed in four different European laboratories working in close collaboration with four European Epilepsy Centers. Computer-based signal analysis methods were retrospectively applied to IEEG recordings performed in four patients undergoing pre-surgical exploration of pharmaco-resistant epilepsy. The four methods elaborated by the different teams to identify the EZ are based either on frequency analysis, on nonlinear signal analysis, on connectivity measures or on statistical parametric mapping of epileptogenicity indices. All methods converge on the identification of EZ in patients that present with fast activity at seizure onset. When traditional visual inspection was not successful in detecting EZ on IEEG, the different signal analysis methods produced highly discordant results. Quantitative analysis of IEEG recordings complement clinical evaluation by contributing to the study of epileptogenic networks during seizures. We demonstrate that the degree of sensitivity of different computer-based methods to detect the EZ in respect to visual EEG inspection depends on the specific seizure pattern.
对于被诊断为药物难治性癫痫的患者,可通过植入颅内电极来确定负责癫痫发作产生的脑区。致痫区(EZ)的识别基于由高素质神经生理学家对颅内脑电图(IEEG)进行的目视检查。已与信号分析领域合作开发了新的基于计算机的定量脑电图分析方法,以加快致痫区的检测。本报告的目的是比较在四个不同欧洲实验室与四个欧洲癫痫中心密切合作开发的不同信号分析方法。基于计算机的信号分析方法被回顾性地应用于对四名接受药物难治性癫痫术前探查患者的IEEG记录。不同团队为识别致痫区所阐述的四种方法分别基于频率分析、非线性信号分析、连通性测量或致痫性指数的统计参数映射。所有方法在识别发作起始时出现快速活动的患者的致痫区方面趋于一致。当传统目视检查在IEEG上未能成功检测到致痫区时,不同的信号分析方法产生了高度不一致的结果。IEEG记录的定量分析通过有助于研究癫痫发作期间的致痫网络来补充临床评估。我们证明,不同基于计算机的方法相对于目视脑电图检查检测致痫区的敏感程度取决于特定的癫痫发作模式。