Statistical information approaches for the modelling of the epileptic brain

  • Authors:

  • Panos M. Pardalos;J. Chris Sackellares;Leonidas D. Iasemidis;Vitality Yatsenko;Mark C. K. Yang;Deng-Shan Shiau;Wanpracha Chaovalitwongse

  • Affiliations:

  • Department of Industrial and Systems Engineering, Center for Applied Optimization, University of Florida and Biomedical Engineering Program, University of Florida, Gainesville, FL;Biomedical Engineering Program, Department of Neuroscience, Department of Neurology, McKnight Brain Institute and Malcolm Randall V.A. Medical Center, Gainesville, FL;Departments of Biomedical Engineering, Arizona State University, Tempe, AZ;Department of Neuroscience, University of Florida, Gainesville, FL;McKnight Brain Institute, University of Florida, Gainesville, FL and Department of Statistics, University of Florida, Gainesville, FL;Department of Neuroscience and McKnight Brain Institute, University of Florida, Gainesville, FL;Department of Industrial and Systems Engineering, Center for Applied Optimization and Department of Neuroscience, University of Florida and McKnight Brain Institute, University of Florida, Gainesv ...

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2003

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Abstract

First, the theory of random process is linked with the statistical description of epileptic human brain process. A statistical information approach to the adaptive analysis of the electroencephalogram (EEG) is proposed. Then, the problem of time window recognition of the global stochastic model based upon Bayesian estimation and the use of global optimization for restricted experimental data are proposed. A robust algorithm for estimating unknown parameters of stochastic models is considered. The ability of nonlinear time-series analysis to extract features from brain EEG signal for detecting epileptic seizures is evaluated.