Abstracts

Rationale: In this study we evaluated the accuracy of automated EEG source imaging (ESI) in localizing the epileptogenic zone from long-term EEG recordings. Methods: Long-term EEG, recorded with the standard 25-electrode array according to the new IFCN standard, from 41 consecutive patients with focal epilepsy who underwent resective surgery, were analyzed blinded to the surgical outcome. The automated analysis consisted out of spike-detection, clustering and source imaging at the half-rising time and at the peak of each spike-cluster. For the source localization individual head-models with six tissue-layers and a distributed inverse technique (sLORETA) were used. We investigated two approached: a quantitative, fully automated approach and a qualitative, semi-automated, approach in which the clinical context was considered.  For the fully automated approach ESI of the cluster with the highest number of spikes, at the half-rising time was considered as result. In addition, a physician involved in the presurgical evaluation of the patients (SB), 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 the location of the resected area and outcome one year after epilepsy surgery. Results: For the fully automated method, the accuracy, sensitivity and specificity with 95% confidence interval were 61% (45-76%), 60% (41-79%) and 63% (39-85%), respectively. In the semi-automated method, the interpretation of the expert physician (SB) changed seven false negatives to true positives. Among them, non-dominant spike clusters were chosen in six patients, while the peak was selected as analysis time point in five patients. This resulted in an increase of the accuracy and sensitivity to 78% (62-89%) and 88% (75-100%), respectively, while the specificity remained 63% (39-85%). Conclusions: Fully-automated ESI achieves similar performance compared to other non-invasive neuroimaging techniques to localize the EZ. Expert interpretation of the ESI results given the clinical context further improves the sensitivity, making it a valuable tool in the presurgical evaluation. Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 660230.