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Optimization Methods and Software Volume 20, 2005 - Issue 2-3: Mathematical diagnostics dedicated to Professor Franco Gianessi on the occasion of his 70th birthday
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Dynamical approaches and multi-quadratic integer programming for seizure prediction

Wanpracha Chaovalitwongse Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA; Center for Applied Optimization, University of Florida, Gainesville, FL, USA; McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Malcolm Randall V.A. Medical Center, Gainesville, FL, USA
,
Panos M. Pardalos Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA; Department of Computer Science, University of Florida, Gainesville, FL, USA; Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA; Center for Applied Optimization, University of Florida, Gainesville, FL, USA
,
Leonidas D. Iasemidis Department of Biomedical Engineering, Arizona State University, Tempe, AZ, USA; Center for Systems Science and Engineering Research, Arizona State University, Tempe, AZ, USA
,
Deng-Shan Shiau Department of Neuroscience, University of Florida, Gainesville, FL, USA; McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Malcolm Randall V.A. Medical Center, Gainesville, FL, USA
&
J. Chris Sackellares Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA; Department of Neuroscience, University of Florida, Gainesville, FL, USA; Department of Neurology, University of Florida, Gainesville, FL, USA; McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Malcolm Randall V.A. Medical Center, Gainesville, FL, USACorrespondence[email protected]
Pages 389-400 | Received 05 Feb 2003, Accepted 09 Sep 2003, Published online: 31 Jan 2007
 
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Abstract

In this article, we present dynamical approaches and multi-quadratic integer programming techniques to study the problem of seizure prediction. The data used in our studies consist of continuous intracranial electroencephalograms (EEGs) from patients with temporal lobe epilepsy. The results of this study can be used as a criterion to pre-select the critical electrode sites that can be used to predict epileptic seizures.

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