TY - JOUR
T1 - Automated ictal EEG source imaging
T2 - A retrospective, blinded clinical validation study
AU - Baroumand, Amir G
AU - Arbune, Anca A
AU - Strobbe, Gregor
AU - Keereman, Vincent
AU - Pinborg, Lars H
AU - Fabricius, Martin
AU - Rubboli, Guido
AU - Gøbel Madsen, Camilla
AU - Jespersen, Bo
AU - Brennum, Jannick
AU - Mølby Henriksen, Otto
AU - Mierlo, Pieter van
AU - Beniczky, Sándor
N1 - Copyright © 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
PY - 2022/9
Y1 - 2022/9
N2 - OBJECTIVE: EEG source imaging (ESI) is a validated tool in the multimodal workup of patients with drug resistant focal epilepsy. However, it requires special expertise and it is underutilized. To circumvent this, automated analysis pipelines have been developed and validated for the interictal discharges. In this study, we present the clinical validation of an automated ESI for ictal EEG signals.METHODS: We have developed an automated analysis pipeline of ictal EEG activity, based on spectral analysis in source space, using an individual head model of six tissues. The analysis was done blinded to all other data. As reference standard, we used the concordance with the resected area and one-year postoperative outcome.RESULTS: We analyzed 50 consecutive patients undergoing epilepsy surgery (34 temporal and 16 extra-temporal). Thirty patients (60%) became seizure-free. The accuracy of the automated ESI was 74% (95% confidence interval: 59.66-85.37%).CONCLUSIONS: Automated ictal ESI has a high accuracy for localizing the seizure onset zone.SIGNIFICANCE: Automating the ESI of the ictal EEG signals will facilitate implementation of this tool in the presurgical evaluation.
AB - OBJECTIVE: EEG source imaging (ESI) is a validated tool in the multimodal workup of patients with drug resistant focal epilepsy. However, it requires special expertise and it is underutilized. To circumvent this, automated analysis pipelines have been developed and validated for the interictal discharges. In this study, we present the clinical validation of an automated ESI for ictal EEG signals.METHODS: We have developed an automated analysis pipeline of ictal EEG activity, based on spectral analysis in source space, using an individual head model of six tissues. The analysis was done blinded to all other data. As reference standard, we used the concordance with the resected area and one-year postoperative outcome.RESULTS: We analyzed 50 consecutive patients undergoing epilepsy surgery (34 temporal and 16 extra-temporal). Thirty patients (60%) became seizure-free. The accuracy of the automated ESI was 74% (95% confidence interval: 59.66-85.37%).CONCLUSIONS: Automated ictal ESI has a high accuracy for localizing the seizure onset zone.SIGNIFICANCE: Automating the ESI of the ictal EEG signals will facilitate implementation of this tool in the presurgical evaluation.
KW - Drug Resistant Epilepsy/diagnostic imaging
KW - Electroencephalography/methods
KW - Humans
KW - Magnetic Resonance Imaging
KW - Retrospective Studies
KW - Seizures/diagnostic imaging
U2 - 10.1016/j.clinph.2021.03.040
DO - 10.1016/j.clinph.2021.03.040
M3 - Article
C2 - 33972159
SN - 1388-2457
VL - 141
SP - 119
EP - 125
JO - Clinical Neurophysiology
JF - Clinical Neurophysiology
ER -