A wavelet-based adaptive filter for removing ECG interference in EMGdi signals

Zhan, Choujun; Yeung, L.F.; Yang, Zhi

doi:10.1016/j.jelekin.2009.07.007

Cited by 45 publications

(45 citation statements)

References 20 publications

(54 reference statements)

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“…The resolution is similar to the one published by Drake and Callaghan (2006). It is difficult to compare the results with the traces of other published results because usually the details were not shown with a high enough resolution (Taelman et al, 2007;Hof, 2009;Guohua et al, 2009;Zhan et al, 2010).…”

Section: Discussionsupporting

confidence: 62%

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Removal of the electrocardiogram signal from surface EMG recordings using non-linearly scaled wavelets

Tscharner

Eskofier

Federolf

2011

Journal of Electromyography and Kinesiology

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Section: Discussionsupporting

confidence: 62%

“…The present method does not require a fine tuning of parameters and can remove ECG signals that have similar amplitude than the EMG signals. For instance, the very efficient, wavelet-based threshold method of Zhan et al (2010) uses four parameters. In this manuscript the cleaned ECG signal was shown in great detail in the area of the ECG.…”

Section: Discussionmentioning

confidence: 99%

Removal of the electrocardiogram signal from surface EMG recordings using non-linearly scaled wavelets

Tscharner

Eskofier

Federolf

2011

Journal of Electromyography and Kinesiology

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“…Then, a wavelet-based adaptive filter was applied to the EMG signals from the RA and ES muscles to remove the ECG artifacts (Zhan et al 2010). Further, the EMG signals were rectified and a moving average filter of 100 ms duration was applied.…”

Section: Methodsmentioning

confidence: 99%

Effects of muscle fatigue on multi-muscle synergies

Singh

Latash

2011

Exp Brain Res

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We studied the effects of fatigue of ankle dorsiflexors on multi-muscle synergies defined as co-varied adjustments of elemental variables (M-modes) that stabilize a task related performance variable (trajectory of the center of pressure, COP). M-modes were defined as muscle groups with parallel changes in activation levels. Healthy participants performed voluntary body sway in the anterior-posterior direction while trying to minimize sway in the medio-lateral direction at 0.25, 0.5 and 0.75 Hz. The trials were repeated before and during fatigue induced with a timed voluntary contraction against a constant load. Factor extraction using the principal component method was used to identify four M-modes within the space of integrated indices of muscle activity. Variance in the M-mode space at different phases across sway cycles was partitioned into two components, one that did not affect the average value of COP shift and the other that did. There were no significant effects of fatigue on variability of performance of the explicit task and on the amplitude of the COP shift. Variance of muscle activation indices and M-mode magnitudes increased during fatigue for muscles (and M-modes) both involved and not involved in the fatiguing exercise. Most of the M-mode variance increase was within the sub-space compatible with the unchanged COP trajectory resulting in an increase of the index of the multi-M-mode synergy. We conclude that one of the adaptive mechanisms to fatigue within a redundant multi-muscle system involves an increase in the variance of activation of non-fatigued muscles with a simultaneous increase in co-variation among muscle activations. The findings can be interpreted within the referent configuration hypothesis on the control of whole-body actions.

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“…Other more complicated methods have also been developed including ECG template subtraction, wavelet thresholding, adaptive filtering, etc. [ 3 – 7 ] With the development of blind source separation techniques, independent component analysis (ICA) has been used for ECG interference removal from surface EMG or other bioelectrical signals [ 8 , 9 ]. A widely used method is the well-known FastICA, which can be combined with other techniques (such as adaptive filter and wavelet analysis [ 10 , 11 ]) for ECG artifact removal.…”

Section: Introductionmentioning

confidence: 99%

FastICA peel-off for ECG interference removal from surface EMG

et al. 2016

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BackgroundMulti-channel recording of surface electromyographyic (EMG) signals is very likely to be contaminated by electrocardiographic (ECG) interference, specifically when the surface electrode is placed on muscles close to the heart.MethodsA novel fast independent component analysis (FastICA) based peel-off method is presented to remove ECG interference contaminating multi-channel surface EMG signals. Although demonstrating spatial variability in waveform shape, the ECG interference in different channels shares the same firing instants. Utilizing the firing information estimated from FastICA, ECG interference can be separated from surface EMG by a “peel off” processing. The performance of the method was quantified with synthetic signals by combining a series of experimentally recorded “clean” surface EMG and “pure” ECG interference.ResultsIt was demonstrated that the new method can remove ECG interference efficiently with little distortion to surface EMG amplitude and frequency. The proposed method was also validated using experimental surface EMG signals contaminated by ECG interference.ConclusionsThe proposed FastICA peel-off method can be used as a new and practical solution to eliminating ECG interference from multichannel EMG recordings.

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A wavelet-based adaptive filter for removing ECG interference in EMGdi signals

Cited by 45 publications

References 20 publications

Removal of the electrocardiogram signal from surface EMG recordings using non-linearly scaled wavelets

Removal of the electrocardiogram signal from surface EMG recordings using non-linearly scaled wavelets

Effects of muscle fatigue on multi-muscle synergies

FastICA peel-off for ECG interference removal from surface EMG

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