• DocumentCode
    1508943
  • Title

    Enhancement of spectral analysis of myoelectric signals during static contractions using wavelet methods

  • Author

    Karlsson, Stefan ; Yu, Jun ; Akay, Metin

  • Author_Institution
    Dept. of Biomed. Eng. & Inf., Univ. Hospital, Umea, Sweden
  • Volume
    46
  • Issue
    6
  • fYear
    1999
  • fDate
    6/1/1999 12:00:00 AM
  • Firstpage
    670
  • Lastpage
    684
  • Abstract
    Introduces wavelet packets as an alternative method for spectral analysis of surface myoelectric (ME) signals. Both computer synthesized and real ME signals are used to investigate the performance. The authors´ simulation results show that wavelet packet estimate has slightly less mean square error (MSE) than Fourier method, and both methods perform similarly on the real data. Moreover, wavelet packets give one some advantages over the traditional methods such as multiresolution of frequency, as well as its potential use for effecting time-frequency decomposition of the nonstationary signals such as the ME signals during dynamic contractions. The authors also introduce wavelet shrinkage method for improving spectral estimates by significantly reducing the MSE´s for both Fourier and wavelet packet methods.
  • Keywords
    electromyography; medical signal processing; spectral analysis; wavelet transforms; EMG analysis; Fourier methods; computer synthesized signals; mean square error; myoelectric signals spectral analysis; static contractions; wavelet methods; wavelet packets; Computational modeling; Frequency; Mean square error methods; Potential well; Signal resolution; Signal synthesis; Spectral analysis; Surface waves; Wavelet analysis; Wavelet packets; Action Potentials; Adult; Algorithms; Analysis of Variance; Bias (Epidemiology); Data Interpretation, Statistical; Fourier Analysis; Humans; Isometric Contraction; Male; Reproducibility of Results; Signal Processing, Computer-Assisted; Time Factors;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/10.764944
  • Filename
    764944