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van Mierlo P, Strobbe G, Keereman V, et al. Automated long-term EEG analysis to localize the epileptogenic zone. Epilepsia Open. 2017;2(3):322-333doi: 10.1002/epi4.12066.
van Mierlo, P., Strobbe, G., Keereman, V., Birot, G., Gadeyne, S., Gschwind, M., Carrette, E., Meurs, A., Van Roost, D., Vonck, K., Seeck, M., Vulliémoz, S., & Boon, P. (2017). Automated long-term EEG analysis to localize the epileptogenic zone. Epilepsia open, 2(3), 322-333. https://doi.org/10.1002/epi4.12066
van Mierlo, Pieter, et al. "Automated long-term EEG analysis to localize the epileptogenic zone." Epilepsia open vol. 2,3 (2017): 322-333. doi: https://doi.org/10.1002/epi4.12066
van Mierlo P, Strobbe G, Keereman V, Birot G, Gadeyne S, Gschwind M, Carrette E, Meurs A, Van Roost D, Vonck K, Seeck M, Vulliémoz S, Boon P. Automated long-term EEG analysis to localize the epileptogenic zone. Epilepsia Open. 2017 Jun 30;2(3):322-333. doi: 10.1002/epi4.12066. eCollection 2017 Sep. PMID: 29588961; PMCID: PMC5862106.
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Epilepsia Open. 2017 Jun 30;2(3):322-333. doi: 10.1002/epi4.12066. eCollection 2017 Sep.

Automated long-term EEG analysis to localize the epileptogenic zone.

Epilepsia open

Pieter van Mierlo, Gregor Strobbe, Vincent Keereman, Gwénael Birot, Stefanie Gadeyne, Markus Gschwind, Evelien Carrette, Alfred Meurs, Dirk Van Roost, Kristl Vonck, Margitta Seeck, Serge Vulliémoz, Paul Boon

Affiliations

  1. Medical Image and Signal Processing Group Department of Electronics and Information Systems Ghent University-iMinds Medical IT Department Ghent Belgium.
  2. Functional Brain Mapping Laboratory Department of Fundamental Neurosciences University of Geneva Geneva Switzerland.
  3. Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology Department of Neurology Ghent University Hospital Ghent Belgium.
  4. Epilepsy and EEG Unit University Hospital of Geneva Geneva Switzerland.
  5. Department of Neurosurgery Ghent University Hospital Ghent Belgium.

PMID: 29588961 PMCID: PMC5862106 DOI: 10.1002/epi4.12066

Abstract

OBJECTIVE: 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.

METHODS: 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.

RESULTS: 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 < 0.05). For patients with Engel class II, III, and IV outcomes, the median distance increased to 36 and 36 mm for cluster 1. Localizing spike cluster 1 at the peak resulted in a sensitivity of 70% and specificity of 100%, positive prediction value (PPV) of 100%, and negative predictive value (NPV) of 53%. Including the results of spike cluster 2 led to an increased sensitivity of 79% NPV of 55% and diagnostic OR of 11.4, while the specificity dropped to 75% and the PPV to 90%.

SIGNIFICANCE: We showed that automated analysis of long-term EEG recordings results in a high sensitivity and specificity to localize the epileptogenic focus.

Keywords: Automated spike detection; Automated spike localization; EEG source imaging; Patient‐specific head model

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