Optimisation and data mining techniques for the screening of epileptic patients

Ya Ju Fan; Wanpracha A. Chaovalitwongse; Chang Chia Liu; Rajesh C. Sachdeo; Leonidas D. Iasemidis; Panos M. Pardalos

doi:10.1504/IJBRA.2009.024036

Optimisation and data mining techniques for the screening of epileptic patients

Ya Ju Fan, Wanpracha A. Chaovalitwongse, Chang Chia Liu, Rajesh C. Sachdeo, Leonidas D. Iasemidis, Panos M. Pardalos

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

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 filter 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.

Original language	English (US)
Pages (from-to)	187-196
Number of pages	10
Journal	International Journal of Bioinformatics Research and Applications
Volume	5
Issue number	2
DOIs	https://doi.org/10.1504/IJBRA.2009.024036
State	Published - Mar 2009
Externally published	Yes

Keywords

Bioinformatics
Cross validation
Data mining
Epilepsy
Euclidean distance
Gaussian kernel

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Clinical Biochemistry
Health Information Management

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10.1504/IJBRA.2009.024036

Cite this

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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 filter 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.",

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AU - Chaovalitwongse, Wanpracha A.

AU - Liu, Chang Chia

AU - Sachdeo, Rajesh C.

AU - Iasemidis, Leonidas D.

AU - Pardalos, Panos M.

PY - 2009/3

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KW - Euclidean distance

KW - Gaussian kernel

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Optimisation and data mining techniques for the screening of epileptic patients

Abstract

Keywords

ASJC Scopus subject areas

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