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dc.contributor.authorWoolfe, Matthew
dc.contributor.authorPrime, David
dc.contributor.authorGillinder, Lisa
dc.contributor.authorRowlands, David
dc.contributor.authorO'keefe, Steven
dc.contributor.authorDionisio, Sasha
dc.date.accessioned2020-02-17T00:36:52Z
dc.date.available2020-02-17T00:36:52Z
dc.date.issued2019
dc.identifier.issn0165-0270
dc.identifier.doi10.1016/j.jneumeth.2019.108347
dc.identifier.urihttp://hdl.handle.net/10072/391534
dc.description.abstractBackground: 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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofjournalJournal of Neuroscience Methods
dc.relation.ispartofvolume325
dc.subject.fieldofresearchNeurosciences
dc.subject.fieldofresearchPsychology
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchcode3209
dc.subject.fieldofresearchcode52
dc.subject.fieldofresearchcode5204
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsBiochemical Research Methods
dc.subject.keywordsBiochemistry & Molecular Biology
dc.titleAutomatic detection of the epileptogenic zone: An application of the fingerprint of epilepsy
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationWoolfe, M; Prime, D; Gillinder, L; Rowlands, D; O'keefe, S; Dionisio, S, Automatic detection of the epileptogenic zone: An application of the fingerprint of epilepsy, Journal of Neuroscience Methods, 2019, 325
dcterms.dateAccepted2019-07-04
dc.date.updated2020-02-17T00:34:34Z
gro.hasfulltextNo Full Text
gro.griffith.authorO'Keefe, Steven G.
gro.griffith.authorRowlands, David D.


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