Literature DB >> 17395995

Real time ECG artifact removal for myoelectric prosthesis control.

Ping Zhou1, Blair Lock, Todd A Kuiken.   

Abstract

The electrocardiogram (ECG) artifact is a major noise source contaminating the electromyogram (EMG) of torso muscles. This study investigates removal of ECG artifacts in real time for myoelectric prosthesis control, a clinical application that demands speed and efficiency. Three methods with simple and fast implementation were investigated. Removal of ECG artifacts by digital high-pass filtering was implemented. The effects of the cutoff frequency and filter order of high-pass filtering on the resulting EMG signal were quantified. An alternative adaptive spike-clipping approach was also developed to dynamically detect and suppress the ECG artifacts in the signal. Finally, the two methods were combined. Experimental surface EMG recordings with different ECG/EMG ratios were used as testing signals to evaluate the proposed methods. As a key parameter for clinical myoelectric prosthesis control, the average rectified amplitude of the signal was used as the performance indicator to quantitatively analyze the EMG content distortion and the ECG artifact suppression imposed by the two methods. Aiming at clinical application, the optimal parameter assignment for each method was determined on the basis of the performance using the suite of testing signals with various ECG/EMG ratios.

Mesh:

Year:  2007        PMID: 17395995     DOI: 10.1088/0967-3334/28/4/006

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  9 in total

1.  Improved myoelectric prosthesis control using targeted reinnervation surgery: a case series.

Authors:  Laura A Miller; Kathy A Stubblefield; Robert D Lipschutz; Blair A Lock; Todd A Kuiken
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2008-02       Impact factor: 3.802

2.  Activation amplitude patterns do not change for back muscles but are altered for abdominal muscles between dominant and non-dominant hands during one-handed lifts.

Authors:  Heather L Butler; Cheryl L Hubley-Kozey; John W Kozey
Journal:  Eur J Appl Physiol       Date:  2009-02-11       Impact factor: 3.078

3.  Adaptive common average filtering for myocontrol applications.

Authors:  Hubertus Rehbaum; Dario Farina
Journal:  Med Biol Eng Comput       Date:  2014-11-12       Impact factor: 2.602

4.  The effect of involuntary motor activity on myoelectric pattern recognition: a case study with chronic stroke patients.

Authors:  Xu Zhang; Yun Li; Xiang Chen; Guanglin Li; William Zev Rymer; Ping Zhou
Journal:  J Neural Eng       Date:  2013-07-17       Impact factor: 5.379

5.  Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees.

Authors:  Yanjuan Geng; Oluwarotimi Williams Samuel; Yue Wei; Guanglin Li
Journal:  Biomed Res Int       Date:  2017-04-24       Impact factor: 3.411

6.  Removal of Electrocardiogram Artifacts From Local Field Potentials Recorded by Sensing-Enabled Neurostimulator.

Authors:  Yue Chen; Bozhi Ma; Hongwei Hao; Luming Li
Journal:  Front Neurosci       Date:  2021-04-12       Impact factor: 4.677

7.  FastICA peel-off for ECG interference removal from surface EMG.

Authors:  Maoqi Chen; Xu Zhang; Xiang Chen; Mingxing Zhu; Guanglin Li; Ping Zhou
Journal:  Biomed Eng Online       Date:  2016-06-13       Impact factor: 2.819

8.  Improving the Robustness of Electromyogram-Pattern Recognition for Prosthetic Control by a Postprocessing Strategy.

Authors:  Xu Zhang; Xiangxin Li; Oluwarotimi Williams Samuel; Zhen Huang; Peng Fang; Guanglin Li
Journal:  Front Neurorobot       Date:  2017-09-27       Impact factor: 2.650

9.  Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity.

Authors:  Evan Campbell; Angkoon Phinyomark; Erik Scheme
Journal:  Sensors (Basel)       Date:  2020-03-13       Impact factor: 3.576

  9 in total

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