Motor unit innervation zone localization based on robust linear regression analysis

Jie Liu*, Sheng Li, Faezeh Jahanmiri-Nezhad, William Zev Rymer, Ping Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

With the aim of developing a flexible and reliable procedure for superficial muscle innervation zone (IZ) localization, we proposed a method to estimate IZ location using surface electromyogram (EMG) based on robust linear regression. Regression lines were used to model the bidirectional propagation pattern of a single motor unit action potential (MUAP) and visualize the trajectory of the MUAP propagation. IZ localization was performed by identifying the origin of the bidirectional MUAP propagation. Robust linear regression and MUAP peak detection, combined with propagation phase reversal identification, may provide an efficient way to estimate IZ location. Our method offers high resolution in locating IZs based on simulation studies and experimental tests. Furthermore, our method is flexible and may also be applied using a relatively small number of EMG channels. A comparative study of the proposed method with the cross-correlation method for IZ localization was conducted. The results obtained with simulated MUAPs and measured spontaneous MUAPs in the biceps brachii muscle in six subjects (four males and two females, 57 ± 10 years old) with amyotrophic lateral sclerosis (ALS). Our method achieved estimation performance comparable to that obtained by using the cross-correlation method but with higher resolution. This study provides an accurate and practical method to estimate IZ location.

Original languageEnglish (US)
Pages (from-to)65-70
Number of pages6
JournalComputers in Biology and Medicine
Volume106
DOIs
StatePublished - Mar 2019

Keywords

  • Innervation zone (IZ)
  • Motor unit action potential (MUAP)
  • Robust linear regression

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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