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

Amir G Baroumand; Anca A Arbune; Gregor Strobbe; Vincent Keereman; Lars H Pinborg; Martin Fabricius; Guido Rubboli; Camilla Gøbel Madsen; Bo Jespersen; Jannick Brennum; Otto Mølby Henriksen; Pieter van Mierlo; Sándor Beniczky

doi:10.1016/j.clinph.2021.03.040

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

Amir G Baroumand, Anca A Arbune, Gregor Strobbe, Vincent Keereman, Lars H Pinborg, Martin Fabricius, Guido Rubboli, Camilla Gøbel Madsen, Bo Jespersen, Jannick Brennum, Otto Mølby Henriksen, Pieter van Mierlo, Sándor Beniczky

2 Citationer (Scopus)

Abstrakt

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.

Originalsprog	Engelsk
Tidsskrift	Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Vol/bind	141
Sider (fra-til)	119-125
Antal sider	7
ISSN	1388-2457
DOI	https://doi.org/10.1016/j.clinph.2021.03.040
Status	Udgivet - sep. 2022

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10.1016/j.clinph.2021.03.040

Citationsformater

Baroumand, A. G., Arbune, A. A., Strobbe, G., Keereman, V., Pinborg, L. H., Fabricius, M., Rubboli, G., Gøbel Madsen, C., Jespersen, B., Brennum, J., Mølby Henriksen, O., Mierlo, P. V., & Beniczky, S. (2022). Automated ictal EEG source imaging: A retrospective, blinded clinical validation study. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 141, 119-125. https://doi.org/10.1016/j.clinph.2021.03.040

Automated ictal EEG source imaging : A retrospective, blinded clinical validation study. / Baroumand, Amir G; Arbune, Anca A; Strobbe, Gregor et al.

I: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, Bind 141, 09.2022, s. 119-125.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

Baroumand, AG, Arbune, AA, Strobbe, G, Keereman, V, Pinborg, LH , Fabricius, M, Rubboli, G, Gøbel Madsen, C , Jespersen, B, Brennum, J, Mølby Henriksen, O, Mierlo, PV & Beniczky, S 2022, 'Automated ictal EEG source imaging: A retrospective, blinded clinical validation study', Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, bind 141, s. 119-125. https://doi.org/10.1016/j.clinph.2021.03.040

@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, Magnetic Resonance Imaging, Seizures/diagnostic imaging, Electroencephalography/methods, Humans, Drug Resistant Epilepsy/diagnostic imaging, Retrospective Studies",

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",

year = "2022",

month = sep,

doi = "10.1016/j.clinph.2021.03.040",

language = "English",

volume = "141",

pages = "119--125",

journal = "Clinical Neurophysiology",

issn = "1388-2457",

publisher = "Elsevier Ireland Ltd",

}

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

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 - Automated

KW - EEG

KW - Epilepsy surgery

KW - Source analysis

KW - Source imaging

KW - Spectral analysis

KW - Magnetic Resonance Imaging

KW - Seizures/diagnostic imaging

KW - Electroencephalography/methods

KW - Humans

KW - Drug Resistant Epilepsy/diagnostic imaging

KW - Retrospective Studies

U2 - 10.1016/j.clinph.2021.03.040

DO - 10.1016/j.clinph.2021.03.040

M3 - Journal article

C2 - 33972159

VL - 141

SP - 119

EP - 125

JO - Clinical Neurophysiology

JF - Clinical Neurophysiology

SN - 1388-2457

ER -

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

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