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 journalArticlepeer-review

4 Scopus citations


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 languageEnglish (US)
Pages (from-to)187-196
Number of pages10
JournalInternational Journal of Bioinformatics Research and Applications
Issue number2
StatePublished - Mar 2009


  • 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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