Deep learning for automated detection of generalized paroxysmal fast activity in Lennox-Gastaut syndrome.

Journal: Epilepsy & behavior : E&B
Published Date: Sep 6, 2023

Abstract

OBJECTIVES: Generalized paroxysmal fast activity (GPFA) is a key electroencephalographic (EEG) feature of Lennox-Gastaut Syndrome (LGS). Automated analysis of scalp EEG has been successful in detecting more typical abnormalities. Automatic detection of GPFA has been more challenging, due to its variability from patient to patient and similarity to normal brain rhythms. In this work, a deep learning model is investigated for detection of GPFA events and estimating their overall burden from scalp EEG.

Authors

  • Ewan S Nurse
    NeuroEngineering Laboratory, Department of Electrical & Electronic Engineering, The University of Melbourne, Melbourne, VIC, Australia, 3010; Centre for Neural Engineering, The University of Melbourne, Melbourne, VIC, Australia, 3010.
  • Linda J Dalic
    Department of Medicine (Austin Hospital), University of Melbourne, Heidelberg, VIC 3084, Australia; Department of Neurology, Austin Health, Heidelberg, VIC 3084, Australia.
  • Shannon Clarke
    Seer Medical, 278 Queensberry Street, Melbourne 3000, Victoria, Australia.
  • Mark Cook
    The University of Melbourne, 3010 Parkville, VIC, Australia.
  • John Archer
    Department of Medicine (Austin Hospital), University of Melbourne, Heidelberg, VIC 3084, Australia; Department of Neurology, Austin Health, Heidelberg, VIC 3084, Australia; The Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC 3084, Australia; Murdoch Children's Research Institute, Parkville, VIC 3052, Australia.

Keywords

External Resources

Popular Topics
Recent Journals