Transparent muscle characterization using quantitative electromyography: Different binarization mappings

Meena Abdelmaseeh, Tsu Wei Chen, Pascal Poupart, Benn Smith, Daniel Stashuk

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Evaluation of patients with suspected neuromuscular disorders is typically based on qualitative visual and auditory assessment of needle detected eletromyographic (EMG) signals; the resulting muscle characterization is subjective and highly dependent on the skill and experience of the examiner. Quantitative electromyography (QEMG) techniques were developed to extract motor unit potential trains (MUPTs) from needle detected EMG signals, and estimate features capturing motor unit potential (MUP) morphology and quantifying morphological consistency across MUPs belonging to the same MUPT. The aim of this study is to improve available methods for obtaining transparent muscle characterizations from features obtained using QEMG techniques. More specifically, we investigate the following. 1) Can the use of binarization mappings improve muscle categorization accuracies of transparent methods? 2) What are the appropriate binarization mappings in terms of accuracy and transparency? Results from four different sets of examined limb muscles (342 muscles in total) demonstrate that four out of the 10 investigated binarization mappings based on transparent characterization methods outperformed the multi-class characterizers based on Gaussian mixture models (GMM) and the corresponding binarization mappings based on GMM. This suggests that the use of an appropriate binarization mapping can overcome the decrease in categorization accuracy associated with quantizing MUPT features, which is necessary to obtain transparent characterizations. This performance gain can be attributed to the use of more relevant features and tuned quantization to obtain more specific binary characterizations.

Original languageEnglish (US)
Article number6687208
Pages (from-to)511-521
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume22
Issue number3
DOIs
StatePublished - May 2014

Keywords

  • Binarization mapping
  • motor unit potential train (MUPT) features
  • muscle characterization
  • needle electromyography
  • transparent muscle characterization

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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