FastICA peel-off for ECG interference removal from surface EMG

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.

[1]  Christopher J. James,et al.  Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis , 2003, IEEE Transactions on Biomedical Engineering.

[2]  Peter Holland,et al.  Removing ECG noise from surface EMG signals using adaptive filtering , 2009, Neuroscience Letters.

[3]  Yong Hu,et al.  ICA-based ECG removal from Surface Electromyography and its effect on Low Back Pain Assessment , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[4]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[5]  D Chaffin,et al.  High-pass filtering to remove electrocardiographic interference from torso EMG recordings. , 1993, Clinical biomechanics.

[6]  Yong Hu,et al.  An automated ECG-artifact removal method for trunk muscle surface EMG recordings. , 2010, Medical engineering & physics.

[7]  Werner Wolf,et al.  New aspects to event-synchronous cancellation of ECG interference: an application of the method in diaphragmatic EMG signals , 2000, IEEE Transactions on Biomedical Engineering.

[8]  Ping Zhou,et al.  A Novel Framework Based on FastICA for High Density Surface EMG Decomposition , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Andreas Daffertshofer,et al.  Removing ECG contamination from EMG recordings: a comparison of ICA-based and other filtering procedures. , 2012, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[10]  Ze Wang.  Fixed-point algorithms for constrained ICA and their applications in fMRI data analysis. , 2011, Magnetic resonance imaging.

[11]  Choujun Zhan,et al.  A wavelet-based adaptive filter for removing ECG interference in EMGdi signals. , 2010, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[12]  Xu Zhang,et al.  ECG artifact removal from EMG recordings using independent component analysis and adapted filter , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[13]  Ping Zhou,et al.  Eliminating cardiac contamination from myoelectric control signals developed by targeted muscle reinnervation. , 2006, Physiological measurement.

[14]  Ping Zhou,et al.  Real time ECG artifact removal for myoelectric prosthesis control , 2007, Physiological measurement.

[15]  Javier Garcia-Casado,et al.  Adaptive filtering of ECG interference on surface EEnGs based on signal averaging , 2006, Physiological measurement.