Towards Improved Design and Evaluation of Epileptic Seizure Predictors.

Journal: IEEE transactions on bio-medical engineering
Published Date:

Abstract

OBJECTIVE: Key issues in the epilepsy seizure prediction research are (1) the reproducibility of results (2) the inability to compare multiple approaches directly. To overcome these problems, the seizure prediction challenge was organized on Kaggle.com. It aimed at establishing benchmarks on a dataset with predefined train, validation, and test sets. Our main objective is to analyze the competition format, and to propose improvements, which would facilitate a better comparison of algorithms. The second objective is to present a novel deep learning approach to seizure prediction and compare it to other commonly used methods using patient centered metrics.

Authors

  • Iryna Korshunova
  • Pieter-Jan Kindermans
  • Jonas Degrave
  • Thibault Verhoeven
    Department of Electronics and Information Systems, Ghent University, Ghent, Belgium. Electronic address: thibault.verhoeven@ugent.be.
  • Benjamin H Brinkmann
    Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.
  • Joni Dambre
    Department of Electronics and Information Systems, Ghent University, Ghent, Belgium.