Frontiers in Physiology (Mar 2023)

Muscle innervation zone estimation from monopolar high-density M-waves using principal component analysis and radon transform

  • Chengjun Huang,
  • Zhiyuan Lu,
  • Maoqi Chen,
  • Cliff S. Klein,
  • Yingchun Zhang,
  • Sheng Li,
  • Sheng Li,
  • Ping Zhou

DOI
https://doi.org/10.3389/fphys.2023.1137146
Journal volume & issue
Vol. 14

Abstract

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This study examined methods for estimating the innervation zone (IZ) of a muscle using recorded monopolar high density M waves. Two IZ estimation methods based on principal component analysis (PCA) and Radon transform (RT) were examined. Experimental M waves, acquired from the biceps brachii muscles of nine healthy subjects were used as testing data sets. The performance of the two methods was evaluated by comparing their IZ estimations with manual IZ detection by experienced human operators. Compared with manual detection, the agreement rate of the estimated IZs was 83% and 63% for PCA and RT based methods, respectively, both using monopolar high density M waves. In contrast, the agreement rate was 56% for cross correlation analysis using bipolar high density M waves. The mean difference in estimated IZ location between manual detection and the tested method was 0.12 ± 0.28 inter-electrode-distance (IED) for PCA, 0.33 ± 0.41 IED for RT and 0.39 ± 0.74 IED for cross correlation-based methods. The results indicate that the PCA based method was able to automatically detect muscle IZs from monopolar M waves. Thus, PCA provides an alternative approach to estimate IZ location of voluntary or electrically-evoked muscle contractions, and may have particular value for IZ detection in patients with impaired voluntary muscle activation.

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