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AuthorAbualsaud, Khalid
AuthorMahmuddin, Massudi
AuthorSaleh, Mohammad
AuthorMohamed, Amr
Available date2016-11-20T07:41:55Z
Publication Date2015
Publication NameThe Scientific World Journal
Identifierhttp://dx.doi.org/10.1155/2015/945689
CitationKhalid Abualsaud, Massudi Mahmuddin, Mohammad Saleh, and Amr Mohamed, “Ensemble Classifier for Epileptic Seizure Detection for Imperfect EEG Data,” The Scientific World Journal, vol. 2015, Article ID 945689, 15 pages, 2015.
ISSN2356-6140
URIhttp://hdl.handle.net/10576/5022
AbstractBrain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities.This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals. This noise-aware signal combination (NSC) ensemble classifier combines four classification models based on their individual performance. The main objective of the proposed classifier is to enhance the classification accuracy in the presence of noisy and incomplete information while preserving a reasonable amount of complexity.The experimental results show the effectiveness of the NSC technique, which yields higher accuracies of 90% for noiseless data compared with 85%, 85.9%, and 89.5% in other experiments. The accuracy for the proposed method is 80% when SNR = 1dB, 84% when SNR = 5dB, and 88% when SNR = 10dB, while the compression ratio (CR) is 85.35% for all of the datasets mentioned.
SponsorNPRP 7-684-1-127, from the Qatar National Research Fund, a member of Qatar Foundation.
Languageen
PublisherHindawi
SubjectElectroencephalography/methods
Epilepsy/physiopathology
Humans
TitleEnsemble Classifier for Epileptic Seizure Detection for Imperfect EEG Data
TypeArticle
Volume Number2015
ESSN1537-744X


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