Classify epileptic EEG signals using weighted complex networks based community structure detection

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.