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

Background: Epilepsy is a brain disorder that is mainly diagnosed by neurologists based on electroencephalogram (EEG) recordings. Epileptic EEG signals are recorded as multichannel signals. A reliable technique for analysing multi-channel EEG signals is in urgent demand for the treatment and diagnosis of patients who have epilepsy and other brain disorders. Method: In this paper, each single EEG channel is partitioned into four segments, with each segment is further divided into small clusters. A set of statistical features are extracted from each cluster. As a result, a vector of all the features from each EEG single channel is obtained. The resulting features vector is then mapped into an undirected weighted network. The modularity of the networks is found to be the best to detect epileptic seizures in EEG signals. Other local and global network features, including clustering coefficients, average degree and closeness centrality, are also extracted and studied. All the network attributes are ranked based on their potential to detect abnormalities in EEG signals. Results: 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, Naive 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. (C) 2017 Elsevier Ltd. All rights reserved.