Annals of Operations Research

, Volume 148, Issue 1, pp 227–250 | Cite as

Electroencephalogram (EEG) time series classification: Applications in epilepsy

  • Wanpracha Art Chaovalitwongse
  • Oleg A. Prokopyev
  • Panos M. Pardalos
Article
First Online:

Abstract

Epilepsy is among the most common brain disorders. Approximately 25–30% of epilepsy patients remain unresponsive to anti-epileptic drug treatment, which is the standard therapy for epilepsy. In this study, we apply optimization-based data mining techniques to classify the brain's normal and epilepsy activity using intracranial electroencephalogram (EEG), which is a tool for evaluating the physiological state of the brain. A statistical cross validation and support vector machines were implemented to classify the brain's normal and abnormal activities. The results of this study indicate that it may be possible to design and develop efficient seizure warning algorithms for diagnostic and therapeutic purposes.

Keywords

Classification EEG Brain dynamics Optimization Epilepsy Support vector machines 
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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Wanpracha Art Chaovalitwongse
    • 1
  • Oleg A. Prokopyev
    • 2
  • Panos M. Pardalos
    • 2
  1. 1.Department of Industrial and Systems EngineeringRutgers UniversityPiscatawayUSA
  2. 2.Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA

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