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Novel Optimization Models for Abnormal Brain Activity Classification

Author

Listed:
  • W. Art Chaovalitwongse

    (Department of Industrial and Systems Engineering, Rutgers University, Piscataway, New Jersey 08854)

  • Ya-Ju Fan

    (Department of Industrial and Systems Engineering, Rutgers University, Piscataway, New Jersey 08854)

  • Rajesh C. Sachdeo

    (Jersey Shore University Medical Center, Neptune, New Jersey 07753)

Abstract

This paper proposes a new classification technique, called support feature machine (SFM), for multidimensional time-series data. The proposed technique was applied to the classification of abnormal brain activity represented in electroencephalograms (EEGs). First, the dynamical properties of EEGs from each electrode were extracted. These dynamical profiles were put in SFM, which is an optimization model that maximizes classification accuracy by selecting electrodes (features) that correctly classify unlabeled EEG samples based on the nearest-neighbor classification rule. The empirical studies were performed on the EEG data sets collected from 10 subjects. The performance of SFM was assessed and compared with the ones achieved by the traditional k -nearest-neighbor classifier and support vector machines (SVMs). The results show that SFM achieved, on average, over 90% correct classification and outperformed other classification techniques. In the validation step, SFM correctly classified unseen preseizure and normal EEGs with over 73% accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:oropre:v:56:y:2008:i:6:p:1450-1460
    DOI: 10.1287/opre.1080.0573
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    References listed on IDEAS

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    1. 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.
    2. P. S. Bradley & Usama M. Fayyad & O. L. Mangasarian, 1999. "Mathematical Programming for Data Mining: Formulations and Challenges," INFORMS Journal on Computing, INFORMS, vol. 11(3), pages 217-238, August.
    3. Olvi L. Mangasarian & W. Nick Street & William H. Wolberg, 1995. "Breast Cancer Diagnosis and Prognosis Via Linear Programming," Operations Research, INFORMS, vol. 43(4), pages 570-577, August.
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    Cited by:

    1. 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.
    2. Onur Seref & Ya-Ju Fan & Wanpracha Art Chaovalitwongse, 2014. "Mathematical Programming Formulations and Algorithms for Discrete k-Median Clustering of Time-Series Data," INFORMS Journal on Computing, INFORMS, vol. 26(1), pages 160-172, February.
    3. Onur Şeref & Ya-Ju Fan & Elan Borenstein & Wanpracha A. Chaovalitwongse, 2018. "Information-theoretic feature selection with discrete $$k$$ k -median clustering," Annals of Operations Research, Springer, vol. 263(1), pages 93-118, April.
    4. Carrizosa, Emilio & Nogales-Gómez, Amaya & Romero Morales, Dolores, 2017. "Clustering categories in support vector machines," Omega, Elsevier, vol. 66(PA), pages 28-37.

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