Authors: Ya-Ju Fan, Wanpracha A. Chaovalitwongse, Chang-Chia Liu, Rajesh C. Sachdeo, Leonidas D. Iasemidis, Panos M. Pardalos
Addresses: 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
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.
Keywords: cross validation; epilepsy; Euclidean distance; Gaussian kernel; bioinformatics; data mining; patient screening; connectivity SVMs; support vector machines; brain connectivity; neurophysiological signals; EEGs; independent component analysis; multi-dimensional time series; classification.
International Journal of Bioinformatics Research and Applications, 2009 Vol.5 No.2, pp.187 - 196
Published online: 24 Mar 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article