Localization of Epileptogenic Zone on Pre-surgical Intracranial EEG Recordings: Toward a Validation of Quantitative Signal Analysis Approaches
Localization of Epileptogenic Zone on Pre-surgical Intracranial EEG Recordings: Toward a Validation of Quantitative Signal Analysis Approaches
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DOI：
10.1007/s10548-014-0380-8
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发表时间：
2015-11-01
期刊：
影响因子：
2.7
通讯作者：
de Curtis, Marco
中科院分区：
文献类型：
--
作者：
Andrzejak, Ralph G.;David, Olivier;de Curtis, Marco
在诊断为耐药性癫痫的患者中，可通过植入颅内电极来确定导致癫痫发作的大脑区域。致痫区（EZ）的识别是基于由高素质的神经生理学家进行的颅内脑电图（IEEG）的目视检查。与信号分析界合作开发了新的基于计算机的定量EEG分析，以加快EZ检测。本报告的目的是比较不同的信号分析方法，在四个不同的欧洲实验室与四个欧洲癫痫中心密切合作。基于计算机的信号分析方法被回顾性地应用于IEEG记录进行术前探查的药物抵抗性癫痫的4例患者。由不同的团队制定的识别EZ的四种方法是基于频率分析，非线性信号分析，连接措施或癫痫指数的统计参数映射。所有方法都集中在癫痫发作时出现快速活动的患者中的EZ识别上。当传统的视觉检查不能成功检测IEEG上的EZ时，不同的信号分析方法产生高度不一致的结果。IEEG记录的定量分析有助于癫痫发作期间致痫网络的研究，从而补充了临床评价。我们证明了不同的基于计算机的方法来检测视觉脑电图检查的EZ的灵敏度取决于特定的癫痫发作模式。
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.