Localization of seizure onset area from intracranial non-seizure EEG by exploiting locally enhanced synchrony
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Dauwels-2009-Localization of seiz.pdf
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Author(s)
Cash, Sydney S.
Eskandar, Emad
Dauwels, Justin H. G.
Date Issued
November 2009
Journal
2009 Engineering in Medicine and Biology Society
Publisher
Institute of Electrical and Electronics Engineers
Citation
Dauwels, J., E. Eskandar, and S. Cash. “Localization of seizure onset area from intracranial non-seizure EEG by exploiting locally enhanced synchrony.” Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE. 2009. 2180-2183. © 2009 IEEE
Version
Final published version
Abstract
For as many as 30% of epilepsy patients, seizures are poorly controlled with medication alone. For some of these patients surgery may be an option: the brain region responsible for seizure onset may be removed surgically. However, this requires accurate delineation of the seizure onset region. Currently, the key to making this determination is seizure EEG. Therefore, EEG recordings must continue until enough seizures are obtained to determine the onset region; this may take about 5 days to several weeks. In some cases these recordings must be done using invasive electrodes, a procedure that includes substantial risk, discomfort and cost. In this paper, techniques are developed that use periods of intracranial non-seizure (ldquorestrdquo) EEG to localize epileptogenic networks. Analysis of intracranial EEG (recorded by surface and/or depth electrodes) of 6 epileptic patients shows that certain EEG channels and hence cortical regions are consistently more synchronous (ldquohypersynchronousrdquo) compared to others. It is shown that hypersynchrony seems to strongly correlate with the seizure onset zone; this phenomenon may in the long term allow to determine the seizure onset area(s) from non-seizure EEG, which in turn would enable shorter hospitalizations or even avoidance of semi-chronic implantations all-together.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
http://dx.doi.org/10.1109/IEMBS.2009.5332447
