Title

Optimisation and data mining techniques for the screening of epileptic patients

Document Type

Article

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.

Medical Subject Headings

Computational Biology (methods); Electroencephalography (methods); Epilepsy (diagnosis); Humans; Information Storage and Retrieval (methods); Signal Processing, Computer-Assisted

Publication Date

1-1-2009

Publication Title

International journal of bioinformatics research and applications

ISSN

1744-5485

Volume

5

Issue

2

First Page

187

Last Page

96

PubMed ID

19324604

Digital Object Identifier (DOI)

10.1504/IJBRA.2009.024036

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