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dc.contributor.authorKharbouch, Alaa
dc.contributor.authorShoeb, Ali
dc.contributor.authorCash, Sydney S.
dc.contributor.authorGuttag, John V.
dc.date.accessioned2015-12-14T19:50:19Z
dc.date.available2015-12-14T19:50:19Z
dc.date.issued2011-11
dc.date.submitted2011-08
dc.identifier.issn15255050
dc.identifier.issn1525-5069
dc.identifier.urihttp://hdl.handle.net/1721.1/100243
dc.description.abstractThis article addresses the problem of real-time seizure detection from intracranial EEG (IEEG). One difficulty in creating an approach that can be used for many patients is the heterogeneity of seizure IEEG patterns across different patients and even within a patient. In addition, simultaneously maximizing sensitivity and minimizing latency and false detection rates has been challenging as these are competing objectives. Automated machine learning systems provide a mechanism for dealing with these hurdles. Here we present and evaluate an algorithm for real-time seizure onset detection from IEEG using a machine-learning approach that permits a patient-specific solution. We extract temporal and spectral features across all intracranial EEG channels. A pattern recognition component is trained using these feature vectors and tested against unseen continuous data from the same patient. When tested on more than 875 hours of IEEG data from 10 patients, the algorithm detected 97% of 67 test seizures of several types with a median detection delay of 5 seconds and a median false alarm rate of 0.6 false alarms per 24-hour period. The sensitivity was 100% for 8 of 10 patients. These results indicate that a sensitive, specific, and relatively short-latency detection system based on machine learning can be employed for seizure detection from EEG using a full set of intracranial electrodes to individual patients. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.en_US
dc.description.sponsorshipCenter for Integration of Medicine and Innovative Technologyen_US
dc.description.sponsorshipQuanta Computer (Firm)en_US
dc.description.sponsorshipCyberonics, Inc.en_US
dc.language.isoen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.yebeh.2011.08.031en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourcePMCen_US
dc.titleAn algorithm for seizure onset detection using intracranial EEGen_US
dc.typeArticleen_US
dc.identifier.citationKharbouch, Alaa, Ali Shoeb, John Guttag, and Sydney S. Cash. “An Algorithm for Seizure Onset Detection Using Intracranial EEG.” Epilepsy & Behavior 22 (December 2011): S29–S35.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorKharbouch, Alaaen_US
dc.contributor.mitauthorGuttag, John V.en_US
dc.relation.journalEpilepsy & Behavioren_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsKharbouch, Alaa; Shoeb, Ali; Guttag, John; Cash, Sydney S.en_US
dc.identifier.orcidhttps://orcid.org/0000-0003-0992-0906
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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