Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4942961 | Expert Systems with Applications | 2017 | 14 Pages |
Abstract
Eight pairs of combinations of EEG signals are classified by the proposed method using four well known classifiers: a least support vector machine, k-means, Naïve Bayes, and K-nearest. The proposed method achieved an average of 98%, 96.5%, 99%, rand 0.012, respectively, for its accuracy, sensitivity, specificity and the false positive rate. Comparisons were made using several existing epileptic seizures detection methods using the same datasets. The obtained results showed that the proposed method was efficient in detecting epileptic seizures in EEG signals.
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Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Mohammed Diykh, Li Yan, Wen Peng,