Automated long-term EEG analysis to localize the epileptogenic zone

• Published in: Epilepsia open 2017-09, Vol.2 (3), p.322-333
• Main Authors: van Mierlo, Pieter, Strobbe, Gregor, Keereman, Vincent, Birot, Gwénael, Gadeyne, Stefanie, Gschwind, Markus, Carrette, Evelien, Meurs, Alfred, Van Roost, Dirk, Vonck, Kristl, Seeck, Margitta, Vulliémoz, Serge, Boon, Paul
• Format: Article
• Language: English
• Subjects: Automation; Convulsions & seizures; Electroencephalography; Epilepsy; Full‐Length Original Research; Localization; Methods; NMR; Nuclear magnetic resonance; Patients; Software; Studies; Surgery
• ISSN: 2470-9239; 2470-9239
• DOI: 10.1002/epi4.12066

We investigated the performance of automatic spike detection and subsequent electroencephalogram (EEG) source imaging to localize the epileptogenic zone (EZ) from long-term EEG recorded during video-EEG monitoring. In 32 patients, spikes were automatically detected in the EEG and clustered according to their morphology. The two spike clusters with most single events in each patient were averaged and localized in the brain at the half-rising time and peak of the spike using EEG source imaging. On the basis of the distance from the sources to the resection and the known patient outcome after surgery, the performance of the automated EEG analysis to localize the EZ was quantified. In 28 out of the 32 patients, the automatically detected spike clusters corresponded with the reported interictal findings. The median distance to the resection in patients with Engel class I outcome was 6.5 and 15 mm for spike cluster 1 and 27 and 26 mm for cluster 2, at the peak and the half-rising time of the spike, respectively. Spike occurrence (cluster 1 vs. cluster 2) and spike timing (peak vs. half-rising) significantly influenced the distance to the resection (p