International Journal of Computational Intelligence Systems

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Volume 11, Issue 1, 2018, Pages 663 - 671

A computer aided analysis scheme for detecting epileptic seizure from EEG data

Authors
Enamul Kabir1, *, Enamul.Kabir@usq.edu.au, Siuly2, *, siuly.siuly@vu.edu.au, Jinli Cao3, cao@latrobe.edu.au, Hua Wang2, Hua.Wang@vu.edu.au
1School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Toowoomba, QLD, Australia
2Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, Australia
3Department: Computer Science and Computer Engineering, Latrobe University, Australia
*Corresponding author.
Corresponding Authors
Received 31 March 2017, Accepted 5 January 2018, Available Online 22 January 2018.
DOI
10.2991/ijcis.11.1.51How to use a DOI?
Keywords
Electroencephalogram; Epileptic seizure; Feature extraction; K-means clustering technique; Classification; Machine-learning techniques
Abstract

This paper presents a computer aided analysis system for detecting epileptic seizure from electroencephalogram (EEG) signal data. As EEG recordings contain a vast amount of data, which is heterogeneous with respect to a time-period, we intend to introduce a clustering technique to discover different groups of data according to similarities or dissimilarities among the patterns. In the proposed methodology, we use K-means clustering for partitioning each category EEG data set (e.g. healthy; epileptic seizure) into several clusters and then extract some representative characteristics from each cluster. Subsequently, we integrate all the features from all the clusters in one feature set and then evaluate that feature set by three well-known machine learning methods: Support Vector Machine (SVM), Naive bayes and Logistic regression. The proposed method is tested by a publicly available benchmark database: ‘Epileptic EEG database’. The experimental results show that the proposed scheme with SVM classifier yields overall accuracy of 100% for classifying healthy vs epileptic seizure signals and outperforms all the recent reported existing methods in the literature. The major finding of this research is that the proposed K-means clustering based approach has an ability to efficiently handle EEG data for the detection of epileptic seizure.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
11 - 1
Pages
663 - 671
Publication Date
2018/01/22
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.11.1.51How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Enamul Kabir
AU  - Siuly
AU  - Jinli Cao
AU  - Hua Wang
PY  - 2018
DA  - 2018/01/22
TI  - A computer aided analysis scheme for detecting epileptic seizure from EEG data
JO  - International Journal of Computational Intelligence Systems
SP  - 663
EP  - 671
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.51
DO  - 10.2991/ijcis.11.1.51
ID  - Kabir2018
ER  -