Combining electromyographic and electrical impedance data sets through machine learning: A study in D2-mdx and wild-type mice.

Journal: Muscle & nerve
Published Date:

Abstract

INTRODUCTION/AIMS: Needle impedance-electromyography (iEMG) assesses the active and passive electrical properties of muscles concurrently by using a novel needle with six electrodes, two for EMG and four for electrical impedance myography (EIM). Here, we assessed an approach for combining multifrequency EMG and EIM data via machine learning (ML) to discriminate D2-mdx muscular dystrophy and wild-type (WT) mouse skeletal muscle.

Authors

  • Sarbesh Pandeya
    Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
  • Benjamin Sanchez
    Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, USA.
  • Janice A Nagy
    Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
  • Seward B Rutkove
    Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.