Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - imec, De Pintelaan 185, 9000 Ghent, Belgium.
Epilog, Vlasgaardstraat 52, 9000 Ghent, Belgium.
Clin Neurophysiol. 2018 Nov;129(11):2403-2410. doi: 10.1016/j.clinph.2018.09.015. Epub 2018 Sep 24.
To evaluate the accuracy of automated EEG source imaging (ESI) in localizing epileptogenic zone.
Long-term EEG, recorded with the standard 25-electrode array of the IFCN, from 41 consecutive patients with focal epilepsy who underwent resective surgery, were analyzed blinded to the surgical outcome. The automated analysis comprised spike-detection, clustering and source imaging at the half-rising time and at the peak of each spike-cluster, using individual head-models with six tissue-layers and a distributed source model (sLORETA). The fully automated approach presented ESI of the cluster with the highest number of spikes, at the half-rising time. In addition, a physician involved in the presurgical evaluation of the patients, evaluated the automated ESI results (up to four clusters per patient) in clinical context and selected the dominant cluster and the analysis time-point (semi-automated approach). The reference standard was location of the resected area and outcome one year after operation.
Accuracy was 61% (95% CI: 45-76%) for the fully automated approach and 78% (95% CI: 62-89%) for the semi-automated approach.
Automated ESI has an accuracy similar to previously reported neuroimaging methods.
Automated ESI will contribute to increased utilization of source imaging in the presurgical evaluation of patients with epilepsy.
评估自动脑电源成像(ESI)定位致痫区的准确性。
对 41 例接受切除术的局灶性癫痫患者连续进行的长期 EEG 记录,采用 IFCN 的标准 25 电极阵列进行记录,分析时对手术结果进行盲法处理。自动分析包括使用具有 6 个组织层的个体头部模型和分布式源模型(sLORETA)在每个棘波簇的半上升时间和峰值处进行棘波检测、聚类和源成像。全自动方法呈现了具有最高棘波数的簇的 ESI,在半上升时间。此外,一位参与患者术前评估的医生在临床背景下评估了自动 ESI 结果(每个患者最多四个簇),并选择了优势簇和分析时间点(半自动方法)。参考标准是切除区域的位置和术后一年的结果。
全自动方法的准确性为 61%(95%CI:45-76%),半自动方法的准确性为 78%(95%CI:62-89%)。
自动 ESI 的准确性与先前报道的神经影像学方法相似。
自动 ESI 将有助于增加源成像在癫痫患者术前评估中的应用。