Classification of needle-EMG resting potentials by machine learning.

Journal: Muscle & nerve
Published Date:

Abstract

INTRODUCTION: The diagnostic importance of audio signal characteristics in needle electromyography (EMG) is well established. Given the recent advent of audio-sound identification by artificial intelligence, we hypothesized that the extraction of characteristic resting EMG signals and application of machine learning algorithms could help classify various 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.