title: Optimisation and data mining techniques for the screening of epileptic patients.
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.
authors.affiliation: Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ 08854, USA. yjfan@eden.rutgers.edu
authors.forename: Ya-Ju
authors.initials: YJ
authors.lastname: Fan
authors.forename: Wanpracha A
authors.initials: WA
authors.lastname: Chaovalitwongse
authors.forename: Chang-Chia
authors.initials: CC
authors.lastname: Liu
authors.forename: Rajesh C
authors.initials: RC
authors.lastname: Sachdeo
authors.forename: Leonidas
authors.initials: L
authors.lastname: Iasemidis
authors.forename: Panos
authors.initials: P
authors.lastname: Pardalos