Motor unit action potential number estimation in the surface electromyogram: wavelet matching method and its performance boundary

Ping Zhou, William Z Rymer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Given that the motor unit action potential (MUAP) originates at some distance below a standard surface electromyography (EMG) electrode, the basic shapes of surface MUAPs can ideally be represented by only a very small number of waveforms or wavelet functions. Based on this determination, we evaluate ways to estimate the number of MUAPs present in standard surface EMG records, using wavelet based matching techniques to identify MUAP occurrences. The reason for this approach is that estimates of the numbers of MUAPs are likely to be a more accurate reflection of the neural command to muscle than are current EMG quantification methods, which treat the EMG as a continuous signal. We further attempt to assess the accuracy and general applicability of wavelet based methods used for this purpose, and the performance boundaries of the counting methods are also explored. We show that the performance of wavelet matching methods is mainly determined by the MUAP superposition rate in the signal. To explore this prediction more directly, we compared the MUAP number estimation results by wavelet matching methods using a highly selective multiple concentric ring surface electrode and a standard single differential surface EMG electrode.

Original languageEnglish (US)
Title of host publicationConference Proceedings - 1st International IEEE EMBS Conference on Neural Engineering
PublisherIEEE Computer Society
Pages336-339
Number of pages4
Volume2003-January
ISBN (Electronic)0780375793
DOIs
StatePublished - Jan 1 2003
Event1st International IEEE EMBS Conference on Neural Engineering - Capri Island, Italy
Duration: Mar 20 2003Mar 22 2003

Other

Other1st International IEEE EMBS Conference on Neural Engineering
Country/TerritoryItaly
CityCapri Island
Period3/20/033/22/03

Keywords

  • Electrodes
  • Electromyography
  • Information analysis
  • Muscles
  • Recruitment
  • Shape
  • Signal analysis
  • Surface discharges
  • Surface morphology
  • Surface waves

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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