Sensitivity of surface EMG-based conduction velocity estimates to local tissue in-homogeneities – influence of the number of channels and inter-channel distance

The aim of this simulation study was to investigate the influence of local tissue in-homogeneities on the estimates of muscle fiber conduction velocity (CV) from surface EMG signals. A recently developed analytical surface EMG model was used to generate simulated surface EMG signals from a planar layered volume conductor, comprised of the muscle tissue and fat layer, with spheres (1 mm radius) in the fat layer of conductivity different from the surrounding tissue. CV was estimated with a maximum likelihood multi-channel approach, varying the number of channels and the inter-channel distance used for the estimate. The action potentials detected along the muscle fiber direction changed shape due to the presence of the in-homogeneities, thus affecting CV estimates. CV estimates were influenced by the location of the in-homogeneities with respect to the fiber and detection electrodes. The maximum percent variation of CV estimates due to the presence of in-homogeneities decreased with increasing number of channels and inter-channel distance: 19.6% (2 channels), 12.1% (3 channels), 6.4% (4 channels), for 5 mm inter-channel distance, and 12.0% (2 channels), 5.2% (3 channels), 2.4% (4 channels), for 10 mm inter-channel distance (for double differential detection). The results were in agreement and explained previous experimental findings. It was concluded that multi-channel methods for CV estimation significantly reduce the sensitivity of CV estimates to tissue in-homogeneities.

[1]  L Arendt-Nielsen,et al.  Measurement of Muscle Fiber Conduction Velocity in Humans: Techniques and Applications , 1989, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[2]  R. Merletti,et al.  Modeling of surface myoelectric signals--Part I: Model implementation. , 1999, IEEE transactions on bio-medical engineering.

[3]  D. Farina,et al.  Estimation of single motor unit conduction velocity from surface electromyogram signals detected with linear electrode arrays , 2001, Medical and Biological Engineering and Computing.

[4]  M Naeije,et al.  Estimation of the action potential conduction velocity in human skeletal muscle using the surface EMG cross-correlation technique. , 1983, Electromyography and clinical neurophysiology.

[5]  J. Gillis,et al.  Mixed boundary value problems in potential theory , 1966 .

[6]  R. Merletti,et al.  Modeling of surface myoelectric signals. I. Model implementation , 1999, IEEE Transactions on Biomedical Engineering.

[7]  Jiri Silny,et al.  Spatial Filtering of Noninvasive Multielectrode EMG: Part I-Introduction to Measuring Technique and Applications , 1987, IEEE Transactions on Biomedical Engineering.

[8]  R. Merletti,et al.  Methods for estimating muscle fibre conduction velocity from surface electromyographic signals , 2004, Medical and Biological Engineering and Computing.

[9]  Dario Farina,et al.  A model for surface EMG generation in volume conductors with spherical inhomogeneities , 2005, IEEE Transactions on Biomedical Engineering.

[10]  T. N. Stevenson,et al.  Fluid Mechanics , 2021, Nature.

[11]  D. Farina,et al.  Reproducibility of muscle‐fiber conduction velocity estimates using multichannel surface EMG techniques , 2004, Muscle & nerve.

[12]  Dario Farina,et al.  Influence of anatomical, physical, and detection-system parameters on surface EMG , 2002, Biological Cybernetics.

[13]  Dario Farina,et al.  Effect of experimental muscle pain on motor unit firing rate and conduction velocity. , 2004, Journal of neurophysiology.

[14]  Dario Farina,et al.  Low-threshold motor unit membrane properties vary with contraction intensity during sustained activation with surface EMG visual feedback. , 2004, Journal of applied physiology.

[15]  N. Dimitrova,et al.  Model of the extracellular potential field of a single striated muscle fibre. , 1974, Electromyography and clinical neurophysiology.

[16]  R Merletti,et al.  Comparison of algorithms for estimation of EMG variables during voluntary isometric contractions. , 2000, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[17]  D. Farina,et al.  Effect of Local In-Homogeneities in the Subcutaneous Tissue on Muscle Fiber Conduction Velocity Estimates Assessed with a Novel Analytical Surface EMG Model , 2004 .

[18]  J. Silny,et al.  Influence of tissue inhomogeneities on noninvasive muscle fiber conduction velocity measurements-investigated by physical and numerical modeling , 1991, IEEE Transactions on Biomedical Engineering.

[19]  L. Mesin,et al.  Advances in surface electromyographic signal simulation with analytical and numerical descriptions of the volume conductor , 2004, Medical and Biological Engineering and Computing.

[20]  R. N. Scott,et al.  Statistics of the myoelectric signal from monopolar and bipolar electrodes , 2006, Medical and biological engineering.

[21]  Kevin C. McGill,et al.  High-Resolution Alignment of Sampled Waveforms , 1984, IEEE Transactions on Biomedical Engineering.

[22]  Jiri Silny,et al.  Spatial Filtering of Noninvasive Multielectrode EMG: Part II-Filter Performance in Theory and Modeling , 1987, IEEE Transactions on Biomedical Engineering.

[23]  C. Disselhorst-Klug,et al.  Non-invasive approach of motor unit recording during muscle contractions in humans , 2000, European Journal of Applied Physiology.

[24]  G V Dimitrov,et al.  Simulation analysis of the ability to estimate motor unit propagation velocity non-invasively by different two-channel methods and types of multi-electrodes. , 2003, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[25]  D. Farina,et al.  Assessment of single motor unit conduction velocity during sustained contractions of the tibialis anterior muscle with advanced spike triggered averaging , 2002, Journal of Neuroscience Methods.