Van Eyndhoven Simon, Hunyadi Borbála, Dupont Patrick, Van Paesschen Wim, Van Huffel Sabine
Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium.
Department of Microelectronics, TUDelft, Delft, Netherlands.
Front Neurol. 2019 Aug 2;10:805. doi: 10.3389/fneur.2019.00805. eCollection 2019.
To improve the accuracy of detecting the ictal onset zone, we propose to enhance the epilepsy-related activity present in the EEG signals, before mapping their BOLD correlates through EEG-correlated fMRI analysis. Based solely on a segmentation of interictal epileptic discharges (IEDs) on the EEG, we train multi-channel Wiener filters (MWF) which enhance IED-like waveforms, and suppress background activity and noisy influences. Subsequently, we use EEG-correlated fMRI to find the brain regions in which the BOLD signal fluctuation corresponds to the filtered signals' time-varying power (after convolving with the hemodynamic response function), and validate the identified regions by quantitatively comparing them to ground-truth maps of the (resected or hypothesized) ictal onset zone. We validate the performance of this novel predictor vs. that of commonly used unitary or power-weighted predictors and a recently introduced connectivity-based metric, on a cohort of 12 patients with refractory epilepsy. The novel predictor, derived from the filtered EEG signals, allowed the detection of the ictal onset zone in a larger percentage of epileptic patients (92% vs. at most 83% for the other predictors), and with higher statistical significance, compared to existing predictors. At the same time, the new method maintains maximal specificity by not producing false positive activations in healthy controls. The findings of this study advocate for the use of the MWF to maximize the signal-to-noise ratio of IED-like events in the interictal EEG, and subsequently use time-varying power as a sensitive predictor of the BOLD signal, to localize the ictal onset zone.
为提高检测发作起始区的准确性，我们建议在通过脑电图相关功能磁共振成像（EEG-correlated fMRI）分析绘制其BOLD相关性之前，增强脑电图信号中存在的癫痫相关活动。仅基于脑电图上发作间期癫痫样放电（IEDs）的分割，我们训练多通道维纳滤波器（MWF），该滤波器可增强类似IED的波形，并抑制背景活动和噪声影响。随后，我们使用EEG-correlated fMRI来查找BOLD信号波动与滤波后信号的时变功率（与血液动力学响应函数卷积后）相对应的脑区，并通过将识别出的区域与（切除或假设的）发作起始区的真实图谱进行定量比较来验证这些区域。我们在一组12例难治性癫痫患者中验证了这种新型预测器与常用的单一或功率加权预测器以及最近引入的基于连通性的指标的性能。与现有预测器相比，源自滤波后脑电图信号的新型预测器能够在更大比例的癫痫患者中检测到发作起始区（92%，而其他预测器最高为83%），且具有更高的统计学意义。同时，新方法通过在健康对照中不产生假阳性激活来保持最大特异性。本研究结果提倡使用MWF来最大化发作间期脑电图中类似IED事件的信噪比，并随后使用时变功率作为BOLD信号的敏感预测指标，以定位发作起始区。
