| An algorithm for seizure onset detection using intracranial EEG. | |
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MedLine Citation:
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PMID: 22078515 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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This 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. |
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Authors:
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Alaa Kharbouch; Ali Shoeb; John Guttag; Sydney S Cash |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Epilepsy & behavior : E&B Volume: 22 Suppl 1 ISSN: 1525-5069 ISO Abbreviation: Epilepsy Behav Publication Date: 2011 Dec |
Date Detail:
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Created Date: 2011-11-14 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 100892858 Medline TA: Epilepsy Behav Country: United States |
Other Details:
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Languages: eng Pagination: S29-35 Citation Subset: IM |
Copyright Information:
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Copyright © 2011 Elsevier Inc. All rights reserved. |
Affiliation:
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Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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