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Electroencephalogram (EEG) time series classification: Applications in epilepsy

Author

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  • Wanpracha Chaovalitwongse
  • Oleg Prokopyev
  • Panos Pardalos

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. Copyright Springer Science + Business Media, LLC 2006

Suggested Citation

  • Wanpracha Chaovalitwongse & Oleg Prokopyev & Panos Pardalos, 2006. "Electroencephalogram (EEG) time series classification: Applications in epilepsy," Annals of Operations Research, Springer, vol. 148(1), pages 227-250, November.
    • Handle: RePEc:spr:annopr:v:148:y:2006:i:1:p:227-250:10.1007/s10479-006-0076-x
    DOI: 10.1007/s10479-006-0076-x
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    References listed on IDEAS

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    1. L.D. Iasemidis & P. Pardalos & J.C. Sackellares & D.-S. Shiau, 2001. "Quadratic Binary Programming and Dynamical System Approach to Determine the Predictability of Epileptic Seizures," Journal of Combinatorial Optimization, Springer, vol. 5(1), pages 9-26, March.
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    Citations

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    Cited by:

    1. Ya-Ju Fan & Wanpracha Chaovalitwongse, 2010. "Optimizing feature selection to improve medical diagnosis," Annals of Operations Research, Springer, vol. 174(1), pages 169-183, February.
    2. Wanpracha Chaovalitwongse, 2009. "Comments on: Optimization and data mining in medicine," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(2), pages 247-249, December.
    3. Luigi Bianchi & Chiara Liti & Giampaolo Liuzzi & Veronica Piccialli & Cecilia Salvatore, 2022. "Improving P300 Speller performance by means of optimization and machine learning," Annals of Operations Research, Springer, vol. 312(2), pages 1221-1259, May.
    4. Wanpracha Art Chaovalitwongse, 2008. "Novel quadratic programming approach for time series clustering with biomedical application," Journal of Combinatorial Optimization, Springer, vol. 15(3), pages 225-241, April.
    5. W. Art Chaovalitwongse & Ya-Ju Fan & Rajesh C. Sachdeo, 2008. "Novel Optimization Models for Abnormal Brain Activity Classification," Operations Research, INFORMS, vol. 56(6), pages 1450-1460, December.
    6. Yerim Choi & Jonghun Park & Dongmin Shin, 2017. "A semi-supervised inattention detection method using biological signal," Annals of Operations Research, Springer, vol. 258(1), pages 59-78, November.

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