Document Detail


Automated diagnosis of epilepsy using EEG power spectrum.
MedLine Citation:
PMID:  22967005     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Interictal electroencephalography (EEG) has clinically meaningful limitations in its sensitivity and specificity in the diagnosis of epilepsy because of its dependence on the occurrence of epileptiform discharges. We have developed a computer-aided diagnostic (CAD) tool that operates on the absolute spectral energy of the routine EEG and has both substantially higher sensitivity and negative predictive value than the identification of interictal epileptiform discharges. Our approach used a multilayer perceptron to classify 156 patients admitted for video-EEG monitoring. The patient population was diagnostically diverse; 87 were diagnosed with either generalized or focal seizures. The remainder of the patients were diagnosed with nonepileptic seizures. The sensitivity was 92% (95% confidence interval [CI] 85-97%) and the negative predictive value was 82% (95% CI 67-92%). We discuss how these findings suggest that this CAD can be used to supplement event-based analysis by trained epileptologists.
Authors:
Wesley T Kerr; Ariana Anderson; Edward P Lau; Andrew Y Cho; Hongjing Xia; Jennifer Bramen; Pamela K Douglas; Eric S Braun; John M Stern; Mark S Cohen
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't     Date:  2012-09-11
Journal Detail:
Title:  Epilepsia     Volume:  53     ISSN:  1528-1167     ISO Abbreviation:  Epilepsia     Publication Date:  2012 Nov 
Date Detail:
Created Date:  2012-11-13     Completed Date:  2013-01-11     Revised Date:  2013-11-06    
Medline Journal Info:
Nlm Unique ID:  2983306R     Medline TA:  Epilepsia     Country:  United States    
Other Details:
Languages:  eng     Pagination:  e189-92     Citation Subset:  IM    
Copyright Information:
Wiley Periodicals, Inc. © 2012 International League Against Epilepsy.
Affiliation:
Medical Scientist Training Program and Department of Biomathematics, University of California at Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90095, U.S.A. wesleytk@ucla.edu
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MeSH Terms
Descriptor/Qualifier:
Diagnosis, Computer-Assisted / methods*
Electroencephalography / methods*
Epilepsy / diagnosis*,  physiopathology*
Humans
Grant Support
ID/Acronym/Agency:
R33 DA026109/DA/NIDA NIH HHS; R33 DA026109/DA/NIDA NIH HHS; T32 GM008042/GM/NIGMS NIH HHS; T32 GM008185/GM/NIGMS NIH HHS; T32 GM008185/GM/NIGMS NIH HHS; T32 GM08042/GM/NIGMS NIH HHS
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