Effect of electrocardiographic contamination on surface electromyography assessment of back muscles.

The purpose of this study was to demonstrate the relative effect of electrocardiography (ECG) on back muscle surface electromyography (SEMG) parameters and their corresponding sensitivity in low back pain (LBP) assessment. Back muscle SEMG activities were recorded from 17 healthy subjects and 18 chronic LBP patients under static postures (straight sitting and upright standing), and dynamic action (flexion-extension). ECG cancellation based on independent component analysis (ICA) method was performed. Root mean square (RMS) and median frequency (MF) of raw and denoised SEMG data were computed respectively. Multiple comparisons were then performed. A consistent trend of change (increased MF and decreased RMS) followed ECG removal was noticed. In particular, in SEMG measurements under static postures, a significant decrease in RMS (p<0.05) and increase in MF (p<0.05) were found in all recording muscle groups. Level of corruption by ECG artifacts on SEMG measurements was found to be more serious and prominent in static postures than that in dynamic action. After ECG removal, significant improvements in the ability of SEMG to discriminate LBP patients from healthy subjects were seen in RMS amplitude recorded while standing (p<0.05) and MF in all measuring conditions (p<0.05). This study provides a more complete understanding on the relative effect of ECG contamination on back muscles SEMG parameters and LBP assessment.

[1]  Carlo J. De Luca,et al.  Use of the surface EMG signal for performance evaluation of back muscles , 1993 .

[2]  Jun Yu,et al.  Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study , 2000, IEEE Transactions on Biomedical Engineering.

[3]  D. Farina,et al.  Myoelectric manifestations of muscle fatigue , 2004 .

[4]  G. Allison Trunk muscle onset detection technique for EMG signals with ECG artefact. , 2003, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[5]  A J Gitter,et al.  AAEM practice topics: Technology assessment: The use of surface EMG in the diagnosis and treatment of nerve and muscle disorders , 1996, Muscle & nerve.

[6]  Paolo Bonato,et al.  Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions , 2001, IEEE Transactions on Biomedical Engineering.

[7]  J. S. Lee,et al.  Evaluation of EMG signals from rehabilitated patients with lower back pain using wavelets. , 1998, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[8]  P. Strobach,et al.  Event-synchronous cancellation of the heart interference in biomedical signals , 1994, IEEE Transactions on Biomedical Engineering.

[9]  P. Dolan,et al.  The use of surface EMG power spectral analysis in the evaluation of back muscle function. , 1997, Journal of rehabilitation research and development.

[10]  Apostolos Georgakis,et al.  Fatigue analysis of the surface EMG signal in isometric constant force contractions using the averaged instantaneous frequency , 2003, IEEE Transactions on Biomedical Engineering.

[11]  G Ebenbichler Dynamic EMG: a clinician's perspective. , 2001, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[12]  C J De Luca,et al.  Classification of back muscle impairment based on the surface electromyographic signal. , 1997, Journal of rehabilitation research and development.

[13]  Roberto Merletti,et al.  Electromyography. Physiology, engineering and non invasive applications , 2005 .

[14]  Carlo J. De Luca,et al.  The Use of Surface Electromyography in Biomechanics , 1997 .

[15]  C Marque,et al.  Adaptive filtering for ECG rejection from surface EMG recordings. , 2005, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[16]  W. Forrest,et al.  Power Spectrum Analyses of Electromyographic Activity: Discriminators in the Differential Assessment of Patients with Chronic Low‐Back Pain , 1991, Spine.

[17]  Yuzhen Cao,et al.  Applying Independent Component Analysis on ECG Cancellation Technique for the Surface Recording of Trunk Electromyography , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[18]  P. Dolan,et al.  Electromyographic Median Frequency Changes During Isometric Contraction of the Back Extensors to Fatigue , 1994, Spine.

[19]  J. Cram,et al.  Introduction to Surface Electromyography , 1998 .

[20]  M. Knaflitz,et al.  Analysis of myoelectric signals recorded during dynamic contractions , 1996 .

[21]  Te-Won Lee,et al.  Independent Component Analysis , 1998, Springer US.

[22]  J. Gotman,et al.  A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification , 2006, Clinical Neurophysiology.

[23]  E L Morin,et al.  Sampling, noise-reduction and amplitude estimation issues in surface electromyography. , 2002, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[24]  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.

[25]  P. Rossini,et al.  Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals , 2004, Clinical Neurophysiology.

[26]  Jack P Callaghan,et al.  Elimination of electrocardiogram contamination from electromyogram signals: An evaluation of currently used removal techniques. , 2006, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[27]  P A Mathieu,et al.  EMG and kinematics of normal subjects performing trunk flexion/extensions freely in space. , 2000, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[28]  Large-Array Surface Electromyography in Low Back Pain: A Pilot Study , 2003, Spine.

[29]  N. Thakor,et al.  Removal of ECG interference from the EEG recordings in small animals using independent component analysis , 2001, Journal of Neuroscience Methods.

[30]  E. Oja,et al.  Independent Component Analysis , 2013 .

[31]  S M McGill,et al.  The importance of normalization in the interpretation of surface electromyography: a proof of principle. , 1999, Journal of manipulative and physiological therapeutics.

[32]  A. Haig,et al.  A meta-analytic review of surface electromyography among persons with low back pain and normal, healthy controls. , 2005, The journal of pain : official journal of the American Pain Society.

[33]  S Conforto,et al.  Real time monitoring of muscular fatigue from dynamic surface myoelectric signals using a complex covariance approach. , 1999, Medical engineering & physics.

[34]  Dario Farina,et al.  Single-Channel Techniques for Information Extraction from the Surface EMG Signal , 2004 .

[35]  R Merletti,et al.  Introduction to this special issue. Intelligent data analysis in electromyography and electroneurography. , 1999, Medical engineering & physics.

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

[37]  A. Fuglsang-Frederiksen The utility of interference pattern analysis , 2000, Muscle & nerve.

[38]  J S Black,et al.  A comparison of ECG cancellation techniques applied to the surface recording of somatosensory evoked potentials. , 1997, Medical engineering & physics.

[39]  D. Goodin,et al.  Ovid: Pullman: Neurology, Volume 55(2).July 25, 2000.171-177 , 2006 .

[40]  G E Caldwell,et al.  Physiology and interpretation of the electromyogram. , 1996, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[41]  C. Pattichis,et al.  Autoregressive and cepstral analyses of motor unit action potentials. , 1999, Medical engineering & physics.

[42]  Christophe Demoulin,et al.  Spinal muscle evaluation in healthy individuals and low-back-pain patients: a literature review. , 2007, Joint, bone, spine : revue du rhumatisme.

[43]  C. D. De Luca,et al.  Spectral electromyographic assessment of back muscles in patients with low back pain undergoing rehabilitation. , 1995, Spine.

[44]  M. Akay,et al.  Analyzing surface myoelectric signals recorded during isokinetic contractions , 2001, IEEE Engineering in Medicine and Biology Magazine.

[45]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.