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Electroencephalogram (EEG) time series classification: Applications in epilepsy

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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.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Rutgers University, 96 Frenlinghuysen Rd., Piscataway, NJ, 08854, USA

    Wanpracha Art Chaovalitwongse

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, 32601, USA

    Oleg A. Prokopyev & Panos M. Pardalos

Authors
  1. Wanpracha Art Chaovalitwongse
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  2. Oleg A. Prokopyev
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  3. Panos M. Pardalos
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Corresponding author

Correspondence to Wanpracha Art Chaovalitwongse.

Additional information

Research was partially supported by the Rutgers Research Council grant-202018, the NSF grants DBI-980821, CCF-0546574, IIS-0611998, and NIH grant R01-NS-39687-01A1.

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Chaovalitwongse, W.A., Prokopyev, O.A. & Pardalos, P.M. Electroencephalogram (EEG) time series classification: Applications in epilepsy. Ann Oper Res 148, 227–250 (2006). https://doi.org/10.1007/s10479-006-0076-x

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  • DOI: https://doi.org/10.1007/s10479-006-0076-x

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