Optimisation and data mining techniques for the screening of epileptic patients

Authors:
Ya-Ju Fan;Wanpracha A. Chaovalitwongse;Chang-Chia Liu;Rajesh C. Sachdeo;Leonidas D. Iasemidis;Panos M. Pardalos
Affiliations:
Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ 08854, USA.;Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ 08854, USA.;Department of Industrial and Systems Engineering and Biomedical Engineering, University of Florida, Gainesville, FL 32611-6595, USA.;Departments of Neurology and Pediatrics, Jersey Shore University Medical Center, Neptune, NJ 07753, USA.;The Harrington Department of Bioengineering, Arizona State University, Tempe, AZ 85287, USA.;Department of Industrial and Systems Engineering and Biomedical Engineering, University of Florida, Gainesville, FL 32611-6595, USA
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2009

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Abstract

Identifying abnormalities or anomalies by visual inspection on neurophysiologic signals such as ElectroEncephaloGrams (EEGs), is extremely challenging. We propose a novel Multi-Dimensional Time Series (MDTS) classification technique, called Connectivity Support Vector Machines (C-SVMs) that integrates brain connectivity network with SVMs. To alter noise in EEG data, Independent Component Analysis based on the Unbiased Quasi Newton Method was applied. C-SVM achieved 94.8% accuracy classifying subjects compared to 69.4% accuracy with standard SVMs. It suggests that C-SVM can be a rapid, yet accurate, technique for online differentiation between epileptic and normal subjects. It may solve other classification MDTS problems too.