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

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.

Methods

iEMG data were obtained from quadriceps of D2-mdx mice, a muscular dystrophy model, and WT animals. EIM data were collected with the animals under deep anesthesia and EMG data collected under light anesthesia, allowing for limited spontaneous movement. Fourier transformation was performed on the EMG data to provide power spectra that were sampl

SUBMITTER: Pandeya S 

PROVIDER: S-EPMC11621948 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature