Deep-learning for seizure forecasting in canines with epilepsy.

Journal: Journal of neural engineering
Published Date: Jun 1, 2019

Abstract

OBJECTIVE: This paper introduces a fully automated, subject-specific deep-learning convolutional neural network (CNN) system for forecasting seizures using ambulatory intracranial EEG (iEEG). The system was tested on a hand-held device (Mayo Epilepsy Assist Device) in a pseudo-prospective mode using iEEG from four canines with naturally occurring epilepsy.

Authors

  • Petr Nejedly
    Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.
  • Vaclav Kremen
    Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.
  • Vladimir Sladky
  • Mona Nasseri
    Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.
  • Hari Guragain
    Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.
  • Petr Klimes
  • Jan Cimbalnik
  • Yogatheesan Varatharajah
    * Electrical and Computer Engineering, University of Illinois at Urbana-Champaign Urbana, IL 61801, USA.
  • 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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