A fast machine learning approach to facilitate the detection of interictal epileptiform discharges in the scalp electroencephalogram.

Journal: Journal of neuroscience methods
Published Date: Jul 13, 2019

Abstract

BACKGROUND: Finding interictal epileptiform discharges (IEDs) in the EEG is a part of diagnosing epilepsy. Automated software for annotating EEGs of patients with suspected epilepsy can therefore help with reaching a diagnosis. A large amount of data is required for training and evaluating an effective IED detection system. IEDs occur infrequently in the most patients' EEG, therefore, interictal EEG recordings contain mostly background waveforms.

Authors

  • Elham Bagheri
    Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore 639798, Singapore. Electronic address: elham001@e.ntu.edu.sg.
  • Jing Jin
    College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
  • Justin Dauwels
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
  • Sydney Cash
    Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Cambridge, MA, USA.
  • M Brandon Westover
    Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.

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