Automatic detection of the epileptogenic zone: An application of the fingerprint of epilepsy.


Journal

Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558

Informations de publication

Date de publication:
01 09 2019
Historique:
received: 10 05 2019
revised: 04 07 2019
accepted: 04 07 2019
pubmed: 23 7 2019
medline: 8 10 2020
entrez: 23 7 2019
Statut: ppublish

Résumé

The successful delineation of the epileptogenic zone in epilepsy monitoring is crucial for achieving seizure freedom after epilepsy surgery. We aim to improve epileptogenic zone localization by utilizing a computer-assisted tool for the automated grading of the seizure activity recorded in various locations for 20 patients undergoing stereo electroencephalography. Their epileptic seizures were processed to extract two potential biomarkers. The concentration of these biomarkers from within each patient's implantation were then graded to identify their epileptogenic zone and were compared to the clinical assessment. Our technique was capable of ranking the clinically defined epileptogenic zone with high accuracy, above 95%, with a true to false positive ratio of 1:1.52, and was effective with both temporal and extra-temporal onset epilepsies. We compared our method to two other groups performing localization using similar biomarkers. Our classification metrics, sensitivity and precision together were comparable to both groups and our overall accuracy from a larger population was also higher then both. Our method is highly accurate, automated and non-parametric providing clinicians another tool that can be used to help identify the epileptogenic zone in patients undergoing the stereo electroencephalography procedure for epilepsy monitoring.

Sections du résumé

BACKGROUND
The successful delineation of the epileptogenic zone in epilepsy monitoring is crucial for achieving seizure freedom after epilepsy surgery.
NEW METHOD
We aim to improve epileptogenic zone localization by utilizing a computer-assisted tool for the automated grading of the seizure activity recorded in various locations for 20 patients undergoing stereo electroencephalography. Their epileptic seizures were processed to extract two potential biomarkers. The concentration of these biomarkers from within each patient's implantation were then graded to identify their epileptogenic zone and were compared to the clinical assessment.
RESULTS
Our technique was capable of ranking the clinically defined epileptogenic zone with high accuracy, above 95%, with a true to false positive ratio of 1:1.52, and was effective with both temporal and extra-temporal onset epilepsies.
COMPARISON WITH EXISTING METHOD
We compared our method to two other groups performing localization using similar biomarkers. Our classification metrics, sensitivity and precision together were comparable to both groups and our overall accuracy from a larger population was also higher then both.
CONCLUSIONS
Our method is highly accurate, automated and non-parametric providing clinicians another tool that can be used to help identify the epileptogenic zone in patients undergoing the stereo electroencephalography procedure for epilepsy monitoring.

Identifiants

pubmed: 31330159
pii: S0165-0270(19)30205-5
doi: 10.1016/j.jneumeth.2019.108347
pii:
doi:

Substances chimiques

Biomarkers 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

108347

Informations de copyright

Copyright © 2019 Elsevier B.V. All rights reserved.

Auteurs

Matthew Woolfe (M)

Advanced Epilepsy Unit, Mater Adult Hospital Brisbane, Queensland, 4101, Australia; School of Engineering and Built Environment, Griffith University, Queensland, 4111, Australia. Electronic address: matthew.woolfe@griffithuni.edu.au.

David Prime (D)

Advanced Epilepsy Unit, Mater Adult Hospital Brisbane, Queensland, 4101, Australia; School of Engineering and Built Environment, Griffith University, Queensland, 4111, Australia.

Lisa Gillinder (L)

Advanced Epilepsy Unit, Mater Adult Hospital Brisbane, Queensland, 4101, Australia.

David Rowlands (D)

School of Engineering and Built Environment, Griffith University, Queensland, 4111, Australia.

Steven O'keefe (S)

School of Engineering and Built Environment, Griffith University, Queensland, 4111, Australia.

Sasha Dionisio (S)

Advanced Epilepsy Unit, Mater Adult Hospital Brisbane, Queensland, 4101, Australia.

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