• DocumentCode
    2086383
  • Title

    Empirical mode decomposition as a tool to remove the function Electrical stimulation artifact from surface electromyograms: Preliminary investigation

  • Author

    Pilkar, R.B. ; Yarossi, Mathew ; Forrest, G.

  • Author_Institution
    Kessler Found., West Orange, NJ, USA
  • fYear
    2012
  • fDate
    Aug. 28 2012-Sept. 1 2012
  • Firstpage
    1847
  • Lastpage
    1850
  • Abstract
    Rectification of surface EMGs during electrical stimulations (ES) is still a problem to be solved. The broad band frequency components of ES artifact overlap with the EMG spectrum, make this task challenging. In this study, we investigate the potential use of empirical mode decomposition (EMD) method to remove the stimulus artifact from surface EMGs collected during such applications. We hypothesize that the EMD algorithm provides a suitable platform for decomposing the EMG signal into physically meaningful intrinsic modes which can be used to isolate ES artifact. Basic EMD is tested on two signals - ES induced EMG and EMG of voluntary contractions added with simulated ES signal. The algorithm isolates the EMG from ES artifact with considerable success. Further, the EMD method along with the energy operator -TKEO gives even better representation of the EMG signal. However, some high frequency data was lost during reconstruction process. Hence, there is further need to investigate the relationship between the EMD parameters and stimulus artifact properties so that the algorithm can be optimized to reconstruct pure artifact free EMG signal with minimum lost of data.
  • Keywords
    electromyography; medical signal processing; signal reconstruction; EMG signal decomposition; empirical mode decomposition; energy operator; functional electrical stimulation artifact; signal reconstruction; surface EMG; surface electromyogram; Data mining; Electrical stimulation; Electromyography; Muscles; Physiology; USA Councils; Adult; Algorithms; Artifacts; Electric Stimulation; Electromyography; Fourier Analysis; Humans; Image Processing, Computer-Assisted; Male; Muscle Contraction; Muscle, Skeletal; Signal Processing, Computer-Assisted; Surface Properties;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
  • Conference_Location
    San Diego, CA
  • ISSN
    1557-170X
  • Print_ISBN
    978-1-4244-4119-8
  • Electronic_ISBN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/EMBC.2012.6346311
  • Filename
    6346311