Artificial intelligence-based classification of motor unit action potentials in real-world needle EMG recordings.

Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Published Date:

Abstract

OBJECTIVE: To develop an artificial neural network (ANN) for classification of motor unit action potential (MUAP) duration in real-word, unselected and uncleaned needle electromyography (n-EMG) recordings.

Authors

  • Deborah Hubers
    Department of Neurology and Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands. Electronic address: d.hubers@amsterdamumc.nl.
  • Wouter Potters
    Department of Neurology and Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands.
  • Olivier Paalvast
    Department of Neurology and Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands.
  • Sterre de Jonge
    Department of Neurology and Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands.
  • Brian Doelkahar
    Department of Neurology and Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands.
  • Martijn Tannemaat
    Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands.
  • Luuk Wieske
    Department of Neurology and Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands; Department of Clinical Neurophysiology, St. Antonius Hospital, Nieuwegein, the Netherlands.
  • Camiel Verhamme
    Department of Neurology and Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands.