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المؤلفAbualsaud, Khalid
المؤلفMahmuddin, Massudi
المؤلفSaleh, Mohammad
المؤلفMohamed, Amr
تاريخ الإتاحة2016-11-20T07:41:55Z
تاريخ النشر2015
اسم المنشورThe Scientific World Journal
المعرّفhttp://dx.doi.org/10.1155/2015/945689
الاقتباسKhalid 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.
الرقم المعياري الدولي للكتاب2356-6140
معرّف المصادر الموحدhttp://hdl.handle.net/10576/5022
الملخصBrain 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.
راعي المشروعNPRP 7-684-1-127, from the Qatar National Research Fund, a member of Qatar Foundation.
اللغةen
الناشرHindawi
الموضوعElectroencephalography/methods
Epilepsy/physiopathology
Humans
العنوانEnsemble Classifier for Epileptic Seizure Detection for Imperfect EEG Data
النوعArticle
رقم المجلد2015
ESSN1537-744X
dc.accessType Open Access


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