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Identification of Preseizure States in Epilepsy: A Data-Driven Approach for Multichannel EEG Recordings

机译:癫痫发作前状态的识别:多通道脑电图记录的数据驱动方法。

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摘要

The retrospective identification of preseizure states usually bases on a time-resolved characterization of dynamical aspects of multichannel neurophysiologic recordings that can be assessed with measures from linear or non-linear time series analysis. This approach renders time profiles of a characterizing measure – so-called measure profiles – for different recording sites or combinations thereof. Various downstream evaluation techniques have been proposed to single out measure profiles that carry potential information about preseizure states. These techniques, however, rely on assumptions about seizure precursor dynamics that might not be generally valid or face the statistical problem of multiple testing. Addressing these issues, we have developed a method to preselect measure profiles that carry potential information about preseizure states, and to identify brain regions associated with seizure precursor dynamics. Our data-driven method is based on the ratio S of the global to local temporal variance of measure profiles. We evaluated its suitability by retrospectively analyzing long-lasting multichannel intracranial EEG recordings from 18 patients that included 133 focal onset seizures, using a bivariate measure for the strength of interactions. In 17/18 patients, we observed S to be significantly correlated with the predictive performance of measure profiles assessed retrospectively by means of receiver-operating-characteristic statistics. Predictive performance was higher for measure profiles preselected with S than for a manual selection using information about onset and spread of seizures. Across patients, highest predictive performance was not restricted to recordings from focal areas, thus supporting the notion of an extended epileptic network in which even distant brain regions contribute to seizure generation. We expect our method to provide further insight into the complex spatial and temporal aspects of the seizure generating process.
机译:癫痫发作状态的回顾性鉴定通常基于多通道神经生理学记录的动力学方面的时间分辨特征,该特征可通过线性或非线性时间序列分析的方法进行评估。这种方法为不同的记录站点或其组合绘制了表征度量的时间曲线,即所谓的度量曲线。已经提出了各种下游评估技术来选出携带有关癫痫发作前状态的潜在信息的测量资料。但是,这些技术依赖于有关癫痫发作前体动力学的假设,这些假设通常可能无效或面临多重测试的统计问题。为了解决这些问题,我们已经开发出一种方法来预先选择带有有关癫痫发作前状态的潜在信息的测量资料,并确定与癫痫发作前体动力学相关的大脑区域。我们的数据驱动方法基于度量配置文件的全局与局部时间方差之比S。我们通过对相互作用强度的双变量测量,回顾性分析了18例患者的长效多通道颅内脑电图记录,包括133种局灶性发作,评估了其适用性。在17/18例患者中,我们观察到S与通过接收者操作特征统计进行回顾性评估的测量结果的预测性能显着相关。对于使用S预先选择的测量配置文件,其预测性能要比使用癫痫发作和扩散信息进行手动选择的预测性能更高。在所有患者中,最高的预测性能不仅限于病灶部位的记录,因此支持了扩大的癫痫网络的概念,其中甚至较远的大脑区域也有助于癫痫发作的发生。我们希望我们的方法能够为癫痫发作过程的复杂时空方面提供进一步的见识。

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