Optimisation and data mining techniques for the screening of epileptic patients
by Ya-Ju Fan, Wanpracha A. Chaovalitwongse, Chang-Chia Liu, Rajesh C. Sachdeo, Leonidas D. Iasemidis, Panos M. Pardalos
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 5, No. 2, 2009

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.

Online publication date: Tue, 24-Mar-2009

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