Deep learning for waveform identification of resting needle electromyography signals.

Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Published Date: Feb 23, 2019

Abstract

OBJECTIVE: Given the recent advent in machine learning and artificial intelligence on medical data analysis, we hypothesized that the deep learning algorithm can classify resting needle electromyography (n-EMG) discharges.

Authors

  • Hiroyuki Nodera
    Department of Neurology, 3-18-15 Kuramotocho, Tokushima City, 770-8503, Japan.
  • Yusuke Osaki
    Department of Neurology, 3-18-15 Kuramotocho, Tokushima City, 770-8503, Japan.
  • Hiroki Yamazaki
    Department of Neurology, 3-18-15 Kuramotocho, Tokushima City, 770-8503, Japan.
  • Atsuko Mori
    Department of Neurology, 3-18-15 Kuramotocho, Tokushima City, 770-8503, Japan.
  • Yuishin Izumi
    Department of Neurology, 3-18-15 Kuramotocho, Tokushima City, 770-8503, Japan.
  • Ryuji Kaji
    Department of Neurology, 3-18-15 Kuramotocho, Tokushima City, 770-8503, Japan.

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