Time/frequency events of surface mechanomyographic signals resolved by nonlinearly scaled wavelets

Travis W. Beck, Vinzenz von Tscharner, Terry J. Housh, Joel T. Cramer, Joseph P. Weir, Moh H. Malek, Michelle Mielke

Research output: Contribution to journalArticle

47 Scopus citations

Abstract

The purpose of this investigation is to introduce a wavelet analysis designed for analyzing short events reflecting bursts of muscle activity in non-stationary mechanomyographic (MMG) signals. A filter bank of eleven nonlinearly scaled wavelets that maintain the optimal combination of time and frequency resolution across the frequency range of MMG signals (5-100 Hz) was used for the analysis. A comparison with the short-time Fourier transform, Wigner-Ville transform and continuous wavelet transform using a test signal with known time-frequency characteristics showed that the MMG wavelet analysis resolved the intensity, timing, and frequencies of events in a more distinct way without overemphasizing high or low frequencies or generating interference terms. The analysis was used to process MMG signals from the vastus lateralis, rectus femoris, and vastus medialis muscles obtained during maximal concentric and eccentric isokinetic movements. Muscular events were observed that were precisely located in time and frequency in a muscle-specific way, thereby showing periods of synergistic contractions of the quadriceps muscles. The MMG wavelet spectra showed different spectral bands for concentric and eccentric isokinetic movements. In addition, the high and low frequency bands seemed to be activated independently during the isokinetic movement. What generates these bands is not yet known, however, the MMG wavelet analysis was able to resolve them, and is therefore applicable to non-stationary MMG signals.

Original languageEnglish (US)
Pages (from-to)255-266
Number of pages12
JournalBiomedical Signal Processing and Control
Volume3
Issue number3
DOIs
StatePublished - Jul 2008

Keywords

  • Electromyography
  • Isokinetic movement
  • Mechanomyography
  • Muscles
  • Stationary signal
  • Time frequency analysis
  • Wavelets

ASJC Scopus subject areas

  • Signal Processing
  • Health Informatics

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