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Automated ictal EEG source imaging: A retrospective, blinded clinical validation study

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@article{d232296feabf4041bbc67ce967d8f78d,
title = "Automated ictal EEG source imaging: A retrospective, blinded clinical validation study",
abstract = "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.",
keywords = "Automated, EEG, Epilepsy surgery, Source analysis, Source imaging, Spectral analysis",
author = "Baroumand, {Amir G} and Arbune, {Anca A} and Gregor Strobbe and Vincent Keereman and Pinborg, {Lars H} and Martin Fabricius and Guido Rubboli and {G{\o}bel Madsen}, Camilla and Bo Jespersen and Jannick Brennum and {M{\o}lby Henriksen}, Otto and Mierlo, {Pieter van} and S{\'a}ndor Beniczky",
note = "Copyright {\textcopyright} 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.",
year = "2021",
month = apr,
day = "27",
doi = "10.1016/j.clinph.2021.03.040",
language = "English",
pages = "1--7",
journal = "Clinical Neurophysiology",
issn = "1388-2457",
publisher = "Elsevier Ireland Ltd",

}

RIS

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 - 2021/4/27

Y1 - 2021/4/27

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 - Automated

KW - EEG

KW - Epilepsy surgery

KW - Source analysis

KW - Source imaging

KW - Spectral analysis

U2 - 10.1016/j.clinph.2021.03.040

DO - 10.1016/j.clinph.2021.03.040

M3 - Journal article

C2 - 33972159

SP - 1

EP - 7

JO - Clinical Neurophysiology

JF - Clinical Neurophysiology

SN - 1388-2457

ER -

ID: 65653300