de Borman Aurélie, Vespa Simone, Tahry Riëm El, Absil P-A
ICTEAM Institute, UCLouvain, Louvain-la-Neuve, Belgium.
Institute of Neuroscience (IoNS), UCLouvain, Brussels, Belgium.
J Neural Eng. 2022 Mar 10;19(2). doi: 10.1088/1741-2552/ac55ad.
The purpose of this study is to localize the seizure onset zone of patients suffering from drug-resistant epilepsy. During the last two decades, multiple studies proposed the use of independent component analysis (ICA) to analyze ictal electroencephalogram (EEG) recordings. This study aims at evaluating ICA potential with quantitative measurements. In particular, we address the challenging step where the components extracted by ICA of an ictal nature must be selected.We considered a cohort of 10 patients suffering from extratemporal lobe epilepsy who were rendered seizure-free after surgery. Different sets of pre-processing parameters were compared and component features were explored to help distinguish ictal components from others. Quantitative measurements were implemented to determine whether some of the components returned by ICA were located within the resection zone and thus likely to be ictal. Finally, an assistance to the component selection was proposed based on the implemented features.For every seizure, at least one component returned by ICA was localized within the resection zone, with the optimal pre-processing parameters. Three features were found to distinguish components localized within the resection zone: the dispersion of their active brain sources, the ictal rhythm power and the contribution to the EEG variance. Using the implemented component selection assistance based on the features, the probability that the first proposed component yields an accurate estimation reaches 51.43% (without assistance: 24.74%). The accuracy reaches 80% when considering the best result within the first five components.This study confirms the utility of ICA for ictal EEG analysis in extratemporal lobe epilepsy, and suggests relevant features to analyze the components returned by ICA. A component selection assistance is proposed to guide clinicians in their choice for ictal components.
本研究的目的是定位耐药性癫痫患者的癫痫发作起始区。在过去二十年中，多项研究提出使用独立成分分析（ICA）来分析发作期脑电图（EEG）记录。本研究旨在通过定量测量评估ICA的潜力。特别是，我们解决了具有挑战性的步骤，即必须选择由ICA提取的具有发作期性质的成分。我们考虑了一组10例患有颞叶外癫痫的患者，他们在手术后无癫痫发作。比较了不同组的预处理参数，并探索了成分特征以帮助区分发作期成分与其他成分。实施了定量测量以确定ICA返回的一些成分是否位于切除区内，因此可能是发作期的。最后，基于所实施的特征提出了对成分选择的辅助方法。对于每次癫痫发作，在最佳预处理参数下，ICA返回的至少一个成分位于切除区内。发现有三个特征可以区分位于切除区内的成分：其活跃脑源的离散度、发作期节律功率以及对EEG方差的贡献。使用基于这些特征实施的成分选择辅助方法，第一个提出的成分产生准确估计的概率达到51.43%（无辅助时为24.74%）。考虑前五个成分中的最佳结果时，准确率达到80%。本研究证实了ICA在颞叶外癫痫发作期EEG分析中的实用性，并提出了分析ICA返回成分的相关特征。提出了一种成分选择辅助方法，以指导临床医生选择发作期成分。
