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

Abstract

AbstractIntroduction/AimsNeedle 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.MethodsiEMG 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 sampled across the frequency range using three different approaches. Random forest‐based, nested ML was applied to the EIM and EMG data sets separately and then together to assess healthy versus disease category classification using a nested cross‐validation procedure.ResultsData from 20 D2‐mdx and 20 WT limbs were analyzed. EIM data fared better than EMG data in differentiating healthy from disease mice with 93.1% versus 75.6% accuracy, respectively. Combining EIM and EMG data sets yielded similar performance as EIM data alone with 92.2% accuracy.DiscussionWe have demonstrated an ML‐based approach for combining EIM and EMG data obtained with an iEMG needle. While EIM‐EMG in combination fared no better than EIM alone with this data set, the approach used here demonstrates a novel method of combining the two techniques to characterize the full electrical properties of skeletal muscle.