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

Diykh, Mohammed and Li, Yan and Wen, Peng (2017) Classify epileptic EEG signals using weighted complex networks based community structure detection. Expert Systems with Applications , 90. pp. 87-100. ISSN 0957-4174

Abstract

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, 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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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to published version, in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 12 Sep 2017 02:54
Last Modified: 24 Apr 2018 01:35
Uncontrolled Keywords: Epileptic EEG signals; modularity; statistical features; weighted complex networks
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
Identification Number or DOI: 10.1016/j.eswa.2017.08.012
URI: http://eprints.usq.edu.au/id/eprint/33008

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