Eliminating cardiac contamination from myoelectric control signals developed by targeted muscle reinnervation

Ping Zhou*, Todd A Kuiken

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

The electrocardiogram (ECG) artifact is a major noise contaminating the myoelectric control signals when using shoulder disarticulation prosthesis. This is an even more significant problem with targeted muscle reinnervation to develop additional myoelectric sites for improved prosthesis control in a bilateral amputee at shoulder disarticulation level. This study aims at removal of ECG artifacts from the myoelectric prosthesis control signals produced from targeted muscle reinnervation. Three ECG artifact removal methods based on template subtracting, wavelet thresholding and adaptive filtering were investigated, respectively. Surface EMG signals were recorded from the reinnervated pectoralis muscles of the amputee. As a key parameter for clinical myoelectric prosthesis control, the amplitude measurement of the signal was used as a performance indicator to evaluate the proposed methods. The feasibility of the different methods for clinical application was also investigated with consideration of the clinical speed requirements and memory limitations of commercial prosthesis controllers.

Original languageEnglish (US)
Article number005
JournalPhysiological Measurement
Volume27
Issue number12
DOIs
StatePublished - Dec 1 2006

Keywords

  • Adaptive filter
  • ECG
  • Myoelectric prosthesis
  • Surface EMG
  • Targeted muscle reinnervation
  • Template subtraction
  • Wavelet transform

ASJC Scopus subject areas

  • Biophysics

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