SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal.

Journal: IEEE transactions on bio-medical engineering
Published Date: Jun 29, 2016

Abstract

OBJECTIVE: This paper describes a data-analytic modeling approach for the prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling.

Authors

  • Han-Tai Shiao
  • Vladimir Cherkassky
    Department of Electrical and Computer Engineering, University of Minnesota, St. Paul, MN, United States of America.
  • Jieun Lee
    Department of Pediatrics, Inje University College of Medicine, Ilsan Paik Hospital, Goyang, Republic of Korea.
  • Brandon Veber
  • Edward E Patterson
    Veterinary Medical Center, University of Minnesota, St. Paul, MN, United States of America.
  • Benjamin H Brinkmann
    Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.
  • Gregory A Worrell
    Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.

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