Original Article
Classification of epilepsy using computational intelligence techniques

https://doi.org/10.1016/j.trit.2016.08.001Get rights and content
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Abstract

This paper deals with a real-life application of epilepsy classification, where three phases of absence seizure, namely pre-seizure, seizure and seizure-free, are classified using real clinical data. Artificial neural network (ANN) and support vector machines (SVMs) combined with supervised learning algorithms, and k-means clustering (k-MC) combined with unsupervised techniques are employed to classify the three seizure phases. Different techniques to combine binary SVMs, namely One Vs One (OvO), One Vs All (OvA) and Binary Decision Tree (BDT), are employed for multiclass classification. Comparisons are performed with two traditional classification methods, namely, k-Nearest Neighbour (k-NN) and Naive Bayes classifier. It is concluded that SVM-based classifiers outperform the traditional ones in terms of recognition accuracy and robustness property when the original clinical data is distorted with noise. Furthermore, SVM-based classifier with OvO provides the highest recognition accuracy, whereas ANN-based classifier overtakes by demonstrating maximum accuracy in the presence of noise.

Keywords

Absence seizure
Discrete wavelet transform
Epilepsy classification
Feature extraction
k-means clustering
k-nearest neighbours
Naive Bayes
Neural networks
Support vector machines

Cited by (0)

Bo Xiao received bachelor and master (Hons.) degree from Chongqing University, China, in 2010 and 2013, respectively. He is currently working towards his Ph.D. degree in King's College London. His current research interests include computational intelligence, T-S fuzzy model based fuzzy control, polynomial fuzzy model based fuzzy control and interval type-2 fuzzy logic.

H. K. Lam received the B.Eng. (Hons.) and Ph.D. degrees from the Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, in 1995 and 2000, respectively. During the period of 2000 and 2005, he worked with the Department of Electronic and Information Engineering at The Hong Kong Polytechnic University as Post-Doctoral Fellow and Research Fellow respectively. He joined as a Lecturer at King's College London in 2005 and currently a Reader. His current research interests include intelligent control systems and computational intelligence.

Xunhe Yin received the bachelor and doctoral degree from Harbin Science and Technology University and Harbin Institute of Technology, in 1989 and 2000, respectively. Currently, he works in Beijing Jiaotong University, Beijing, China as Professor. His current research interests include networked control systems; communication, control, and security in smart grid; control and security of cyber-physical system; communication and control technologies in smart traffic systems; intelligent control theory with applications for communication, networks and other areas. He has published more than 50 journals and conference papers.

Gaoxiang Ouyang received the B.S. degree in automation and the M.S. degree in control theory and control engineering both from the Yanshan University, Hebei, China, in 2002 and 2004, respectively, and the Ph.D. degree from the Department of Manufacturing Engineering, City University of Hong Kong, in 2010. He currently serves as an Associate Professor in the School of Brain and Cognitive Sciences, Beijing Normal University, Beijing, China. His research interests include biosignal analysis, neural engineering, and dynamics system.

Peer review under responsibility of Chongqing University of Technology.