The aim of this study was to remove the two principal
noises which disturb the surface electromyography signal
(Diaphragm). These signals are the electrocardiogram ECG artefact
and the power line interference artefact. The algorithm proposed
focuses on a new Lean Mean Square (LMS) Widrow adaptive
structure. These structures require a reference signal that is correlated
with the noise contaminating the signal. The noise references are
then extracted : first with a noise reference mathematically
constructed using two different cosine functions; 50Hz (the
fundamental) function and 150Hz (the first harmonic) function for
the power line interference and second with a matching pursuit
technique combined to an LMS structure for the ECG artefact
estimation. The two removal procedures are attained without the use
of supplementary electrodes. These techniques of filtering are
validated on real records of surface diaphragm electromyography
signal. The performance of the proposed methods was compared with
already conducted research results.
[1] Deng.Y, Wolf W, Schnell R, Appel U. New aspects to eventsynchronous
cancellation of ECG interference: an application of the
method in diaphragmatic EMG signals. IEEE Trans Biomed Eng. 2000;
47(9):1177-1184.
[2] Sinderby. C, Lindström. L, Grassino. A.E. Automatic assessment of
electromyogram quality. Automatic EMG analysis. Journal of Applied
Physiology. 1995; (79): 1803-1815.
[3] Marque C, Bisch C, Dantas R, Elayoubi S, Brosse V, Perot C. Adaptive
filtering for ECG rejection from surface EMG recordings. Journal of
electromyography and kinesiology 2005; (15):310-315.
[4] Levkov C, Mihov G, Ivanov R, Dascalov I, Christov I, Dotsinsky I.
Removal of power-line interference from the ECG: a review of the
subtraction procedure. Biomedical Engineering Online 2005, 4:50
doi:10.1186/1475-925X-4-50.
[5] Gideon.F, Inbar & Antoine.E Noujaim . On surface EMG spectral
characterization and its application to diagnostic classification. IEEE
Transactions on Biomedical Engineering, 1984; 31(9):597-604.
[6] Hualou Liang, Zhiyue Lin, Fuliang Yin. Removal of ECG contamination
from Non linear Analysis 2005; (63): 745-753.
[7] Drake J D M, Callaghan J P. Elimination of electromyogram
contamination from electromyogram signals: An evaluation of currently
used removal techniques. Journal of electromyography and kinesiology
2006; (16):175-187.
[8] Huhta J C, Wbester J G. 60 Hz interference in electrocardiography.
IEEE Trans Biomed Eng. 1973; 20:91-100.
[9] Metting Van Rijn, Peper A, Grimbergen CA. High-quality recordings of
bioelectrical events, Part 1: Interference reduction, theory and practice."
Med Biol Eng Comput. 1990; 28:389-397.
[10] Redfern. M.S, Hughes.R.E, Chaffin. D.B. High-Pass filtering to remove
electrocardiographic interference from torso EMG recordings. J Clin
Biomech 1993; (8) : 44-48.
[11] Widrow B, John R, Glover J.R, McCool & al. Adaptive noise cancelling
principles and applications. Proceeding of the IEEE. 1975; 63 (12):1692-
1716.
[12] Widrow B, McCool .J.M, Larimore.M.G, Richard Johnson.G. Stationary
and nonstationary learning characteristics of the LMS adaptive Filter.
Proceeding of the IEEE. 1976; 64 (8): 1151-1162.
[13] Deluca.C.J, Fever.R.S, and Stulen.F.B. Pasteless electrode for clinical
use. Med Biol Eng Comput .1979; (17) : 387-390.
[14] Levkov.CH. Fast integer coefficient FIR filter to remove the A.C
interference and the high frequency noise component in biological
signals.Med Biol Eng Comput. 1989; (27) :330-332.
[15] Kunt. M, Rey.H, Ligtenberg.A. Pre-processing of electrocardiograms by
digital technics. Signal Processing. 1982; (4) : 215-222.
[16] Furns. G.S, Tompkins. W.J. A learning filter for removing noise
interference. IEEE Trans Biomed Eng. 1983; (30) : 234-235.
[17] Baratta RV, Solomonow M, Zhou B-H, Zhu M. Method to reduce the
variability of EMG power spectrum estimates, Journal of
electromyography and kinesiology 1998; 8 :279-285.
[18] Bensaadoun Y, Raoof K, Novakov E. Elemination du 50Hz du signal
ECG par filtrage adaptatif multidimensionnel. Innov. Techn .Biol .Med,
1994;15 (6) :750-759.
[19] Bahoura M. Hassani S.G. Lee et M. Hubin. Modification de la méthode
de Widrow pour l'élimination de l'interférence 50 Hz du signal ECG.
Innov. Tech. Biol. Med. 1997; 18 (2): 119-127.
[20] Lindström. L.H and Magnusson. H.N. Interpretation of myoelectric
power spectral model and its application. IEEE Trans Biomed Eng.
1976; 65 (5): 653-662.
[21] Raoof K. Traitement du signal electromyographique des muscles
respiratoires et estimation des paramètres en temps réel. Joseph fourier
University of Grenoble France Phd thesis 1993.
[22] Yacoub S, Ben Brahim J, Ketata R, Gumery P Y, Raoof K. Real time
Multidimensional treatments of surface electromyographic signals by
electrodes array. Innov. Techn .Biol .Med. 1997;18 (4) :267-275.
[23] Mallat S and Zhang. Z. Matching pursuit with time frequency
dictionaries. IEEE Trans Signal Processing. 1993 ; (41) 3397-3415.
[24] Mallat S. Une exploration des signaux en ondelette . Ecole
polytechnique 2000.
[25] Davis G, Mallat S and Zhang Z. Adaptive time-frequency
decompositions. Optical Engineering, 1994 ; 33 (7): 2183-2191.
[26] Cherkassky V, Kilts S. Myopotential denoising of ECG signals using
wavelet thresholding methods. Neural Networks 2001; 14 (8): 1129-
1137.
[27] Ping Zhou, Todd A Kuiken. Eliminating cardiac contamination from
myoelectric control signals developed by targeted muscle reinnervation.
Physiological Measurement 2006; 27: 1311-1327.
[1] Deng.Y, Wolf W, Schnell R, Appel U. New aspects to eventsynchronous
cancellation of ECG interference: an application of the
method in diaphragmatic EMG signals. IEEE Trans Biomed Eng. 2000;
47(9):1177-1184.
[2] Sinderby. C, Lindström. L, Grassino. A.E. Automatic assessment of
electromyogram quality. Automatic EMG analysis. Journal of Applied
Physiology. 1995; (79): 1803-1815.
[3] Marque C, Bisch C, Dantas R, Elayoubi S, Brosse V, Perot C. Adaptive
filtering for ECG rejection from surface EMG recordings. Journal of
electromyography and kinesiology 2005; (15):310-315.
[4] Levkov C, Mihov G, Ivanov R, Dascalov I, Christov I, Dotsinsky I.
Removal of power-line interference from the ECG: a review of the
subtraction procedure. Biomedical Engineering Online 2005, 4:50
doi:10.1186/1475-925X-4-50.
[5] Gideon.F, Inbar & Antoine.E Noujaim . On surface EMG spectral
characterization and its application to diagnostic classification. IEEE
Transactions on Biomedical Engineering, 1984; 31(9):597-604.
[6] Hualou Liang, Zhiyue Lin, Fuliang Yin. Removal of ECG contamination
from Non linear Analysis 2005; (63): 745-753.
[7] Drake J D M, Callaghan J P. Elimination of electromyogram
contamination from electromyogram signals: An evaluation of currently
used removal techniques. Journal of electromyography and kinesiology
2006; (16):175-187.
[8] Huhta J C, Wbester J G. 60 Hz interference in electrocardiography.
IEEE Trans Biomed Eng. 1973; 20:91-100.
[9] Metting Van Rijn, Peper A, Grimbergen CA. High-quality recordings of
bioelectrical events, Part 1: Interference reduction, theory and practice."
Med Biol Eng Comput. 1990; 28:389-397.
[10] Redfern. M.S, Hughes.R.E, Chaffin. D.B. High-Pass filtering to remove
electrocardiographic interference from torso EMG recordings. J Clin
Biomech 1993; (8) : 44-48.
[11] Widrow B, John R, Glover J.R, McCool & al. Adaptive noise cancelling
principles and applications. Proceeding of the IEEE. 1975; 63 (12):1692-
1716.
[12] Widrow B, McCool .J.M, Larimore.M.G, Richard Johnson.G. Stationary
and nonstationary learning characteristics of the LMS adaptive Filter.
Proceeding of the IEEE. 1976; 64 (8): 1151-1162.
[13] Deluca.C.J, Fever.R.S, and Stulen.F.B. Pasteless electrode for clinical
use. Med Biol Eng Comput .1979; (17) : 387-390.
[14] Levkov.CH. Fast integer coefficient FIR filter to remove the A.C
interference and the high frequency noise component in biological
signals.Med Biol Eng Comput. 1989; (27) :330-332.
[15] Kunt. M, Rey.H, Ligtenberg.A. Pre-processing of electrocardiograms by
digital technics. Signal Processing. 1982; (4) : 215-222.
[16] Furns. G.S, Tompkins. W.J. A learning filter for removing noise
interference. IEEE Trans Biomed Eng. 1983; (30) : 234-235.
[17] Baratta RV, Solomonow M, Zhou B-H, Zhu M. Method to reduce the
variability of EMG power spectrum estimates, Journal of
electromyography and kinesiology 1998; 8 :279-285.
[18] Bensaadoun Y, Raoof K, Novakov E. Elemination du 50Hz du signal
ECG par filtrage adaptatif multidimensionnel. Innov. Techn .Biol .Med,
1994;15 (6) :750-759.
[19] Bahoura M. Hassani S.G. Lee et M. Hubin. Modification de la méthode
de Widrow pour l'élimination de l'interférence 50 Hz du signal ECG.
Innov. Tech. Biol. Med. 1997; 18 (2): 119-127.
[20] Lindström. L.H and Magnusson. H.N. Interpretation of myoelectric
power spectral model and its application. IEEE Trans Biomed Eng.
1976; 65 (5): 653-662.
[21] Raoof K. Traitement du signal electromyographique des muscles
respiratoires et estimation des paramètres en temps réel. Joseph fourier
University of Grenoble France Phd thesis 1993.
[22] Yacoub S, Ben Brahim J, Ketata R, Gumery P Y, Raoof K. Real time
Multidimensional treatments of surface electromyographic signals by
electrodes array. Innov. Techn .Biol .Med. 1997;18 (4) :267-275.
[23] Mallat S and Zhang. Z. Matching pursuit with time frequency
dictionaries. IEEE Trans Signal Processing. 1993 ; (41) 3397-3415.
[24] Mallat S. Une exploration des signaux en ondelette . Ecole
polytechnique 2000.
[25] Davis G, Mallat S and Zhang Z. Adaptive time-frequency
decompositions. Optical Engineering, 1994 ; 33 (7): 2183-2191.
[26] Cherkassky V, Kilts S. Myopotential denoising of ECG signals using
wavelet thresholding methods. Neural Networks 2001; 14 (8): 1129-
1137.
[27] Ping Zhou, Todd A Kuiken. Eliminating cardiac contamination from
myoelectric control signals developed by targeted muscle reinnervation.
Physiological Measurement 2006; 27: 1311-1327.
@article{"International Journal of Electrical, Electronic and Communication Sciences:56967", author = "Slim Yacoub and Kosai Raoof", title = "Noise Removal from Surface Respiratory EMG Signal", abstract = "The aim of this study was to remove the two principal
noises which disturb the surface electromyography signal
(Diaphragm). These signals are the electrocardiogram ECG artefact
and the power line interference artefact. The algorithm proposed
focuses on a new Lean Mean Square (LMS) Widrow adaptive
structure. These structures require a reference signal that is correlated
with the noise contaminating the signal. The noise references are
then extracted : first with a noise reference mathematically
constructed using two different cosine functions; 50Hz (the
fundamental) function and 150Hz (the first harmonic) function for
the power line interference and second with a matching pursuit
technique combined to an LMS structure for the ECG artefact
estimation. The two removal procedures are attained without the use
of supplementary electrodes. These techniques of filtering are
validated on real records of surface diaphragm electromyography
signal. The performance of the proposed methods was compared with
already conducted research results.", keywords = "Surface EMG, Adaptive, Matching Pursuit, Powerline interference.", volume = "2", number = "2", pages = "259-8", }
