Accurate identification of EEG recordings with interictal epileptiform discharges using a hybrid approach: Artificial intelligence supervised by human experts.

Journal: Epilepsia
Published Date: Mar 7, 2022

Abstract

OBJECTIVE: To evaluate the diagnostic performance of artificial intelligence (AI)-based algorithms for identifying the presence of interictal epileptiform discharges (IEDs) in routine (20-min) electroencephalography (EEG) recordings.

Authors

  • Mustafa Aykut Kural
    Department of Clinical Neurophysiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
  • Jin Jing
    Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
  • Franz Fürbass
    Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Vienna, Austria.
  • Hannes Perko
    Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Vienna, Austria.
  • Erisela Qerama
    Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.
  • Birger Johnsen
    Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.
  • Steffen Fuchs
    Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.
  • M Brandon Westover
    Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
  • Sándor Beniczky
    Department of Clinical Neurophysiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark. Electronic address: sbz@filadelfia.dk.

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