Determination of muscle fatigue in clinical practices using dynamically embedded signals

P.S. Slack, Xianghong Ma

Research output: Contribution to journalArticlepeer-review


This paper explores a new method of analysing muscle fatigue within the muscles predominantly used during microsurgery. The captured electromyographic (EMG) data retrieved from these muscles are analysed for any defining patterns relating to muscle fatigue. The analysis consists of dynamically embedding the EMG signals from a single muscle channel into an embedded matrix. The muscle fatigue is determined by defining its entropy characterized by the singular values of the dynamically embedded (DE) matrix. The paper compares this new method with the traditional method of using mean frequency shifts in the EMG signal's power spectral density. Linear regressions are fitted to the results from both methods, and the coefficients of variation of both their slope and point of intercept are determined. It is shown that the complexity method is slightly more robust in that the coefficient of variation for the DE method has lower variability than the conventional method of mean frequency analysis.
Original languageEnglish
Pages (from-to)41-50
Number of pages10
JournalProceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine
Issue number1
Publication statusPublished - 1 Jan 2008


  • microsurgery
  • muscle fatigue
  • electromyographic signal processing
  • dynamic embedding
  • Shannon entropy


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