Volume conduction in an anatomically based surface EMG model

A finite-element model to simulate surface electromyography (EMG) in a realistic human upper arm is presented. The model is used to explore the effect of limb geometry on surface-detected muscle fiber action potentials. The model was based on magnetic resonance images of the subject's upper arm and includes both resistive and capacitive material properties. To validate the model geometry, experimental and simulated potentials were compared at different electrode sites during the application of a subthreshold sinusoidal current source to the skin surface. Of the material properties examined, the closest approximation to the experimental data yielded a mean root-mean-square (rms) error of the normalized surface potential of 18% or 27%, depending on the site of the applied source. Surface-detected action potentials simulated using the realistic volume conductor model and an idealized cylindrical model based on the same limb geometry were then compared. Variation in the simulated limb geometry had a considerable effect on action potential shape. However, the rate of decay of the action potential amplitude with increasing distance from the fiber was similar in both models. Inclusion of capacitive material properties resulted in temporal low-pass filtering of the surface action potentials. This effect was most pronounced in the end-effect components of action potentials detected at locations far from the active fiber. It is concluded that accurate modeling of the limb geometry, asymmetry, tissue capacitance and fiber curvature is important when the specific action potential shapes are of interest. However, if the objective is to examine more qualitative features of the surface EMG signal, then an idealized volume conductor model with appropriate tissue thicknesses provides a close approximation.

[1]  O. A. Nikitin,et al.  Neither high-pass filtering nor mathematical differentiation of the EMG signals can considerably reduce cross-talk. , 2002, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[2]  F. Gielen,et al.  The electrical conductivity of skeletal muscle tissue. Experimental results of different muscles in vivo , 1984, Clinical Neurology and Neurosurgery.

[3]  Dario Farina,et al.  A novel approach for precise simulation of the EMG signal detected by surface electrodes , 2001, IEEE Trans. Biomed. Eng..

[4]  L.H. Lindstrom,et al.  Interpretation of myoelectric power spectra: A model and its applications , 1977, Proceedings of the IEEE.

[5]  R. W. Lau,et al.  The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. , 1996, Physics in medicine and biology.

[6]  D. Stegeman,et al.  Finite limb dimensions and finite muscle length in a model for the generation of electromyographic signals. , 1991, Electroencephalography and clinical neurophysiology.

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

[8]  H B Boom,et al.  Calculation and registration of the same motor unit action potential. , 1982, Electroencephalography and clinical neurophysiology.

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

[10]  A. van Oosterom,et al.  The effect of torso inhomogeneities on body surface potentials quantified using "tailored" geometry. , 1989, Journal of electrocardiology.

[11]  R. W. Lau,et al.  The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues. , 1996, Physics in medicine and biology.

[12]  Todd A. Kuiken,et al.  A multiple-layer finite-element model of the surface EMG signal , 2002, IEEE Transactions on Biomedical Engineering.

[13]  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.

[14]  A T Barker,et al.  Surface electromyography using electrode arrays: A study of motor neuron disease , 2001, Muscle & nerve.

[15]  Daniel W. Stashuk,et al.  Detection of motor unit action potentials with surface electrodes: influence of electrode size and spacing , 1992, Biological Cybernetics.

[16]  Jacques Duchêne,et al.  A model of EMG generation , 2000, IEEE Transactions on Biomedical Engineering.

[17]  V. Hentz,et al.  Action potentials of curved nerves in finite limbs , 1995, IEEE Transactions on Biomedical Engineering.

[18]  J. Le,et al.  Method to reduce blur distortion from EEG's using a realistic head model , 1993, IEEE Transactions on Biomedical Engineering.

[19]  B. K. van Veen,et al.  Influence of inhomogeneities in muscle tissue on single-fibre action potentials: a model study , 1997, Medical and Biological Engineering and Computing.

[20]  P. Lawrence,et al.  On Modeling the Single Motor Unit Action Potential , 1978, IEEE Transactions on Biomedical Engineering.

[21]  K. L. Boon,et al.  Electrical conductivity of skeletal muscle tissue: Experimental results from different musclesin vivo , 1984, Medical and Biological Engineering and Computing.

[22]  Catherine Disselhorst-Klug,et al.  Simulation analysis of the ability of different types of multi-electrodes to increase selectivity of detection and to reduce cross-talk. , 2003, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[23]  R. Plonsey Action potential sources and their volume conductor fields , 1977, Proceedings of the IEEE.

[24]  C L Vaughan,et al.  Spectral compression of the electromyographic signal due to decreasing muscle fiber conduction velocity. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[25]  L. Geddes,et al.  The specific resistance of biological material—A compendium of data for the biomedical engineer and physiologist , 1967, Medical and biological engineering.

[26]  K. Foster,et al.  Dielectric properties of tissues and biological materials: a critical review. , 1989, Critical reviews in biomedical engineering.

[27]  P. Rosenfalck Intra- and extracellular potential fields of active nerve and muscle fibres. A physico-mathematical analysis of different models. , 1969, Acta physiologica Scandinavica. Supplementum.

[28]  D. Stegeman,et al.  Volume conduction models for surface EMG; confrontation with measurements. , 1997, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

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

[30]  T. Yamamoto,et al.  Electrical properties of the epidermal stratum corneum. , 1973, Medical & biological engineering.

[31]  D. B. Heppner,et al.  Considerations of quasi-stationarity in electrophysiological systems. , 1967, The Bulletin of mathematical biophysics.

[32]  C Gabriel,et al.  The dielectric properties of biological tissues: I. Literature survey. , 1996, Physics in medicine and biology.

[33]  L. Geddes,et al.  The specific resistance of canine blood at body temperature. , 1973, IEEE transactions on bio-medical engineering.

[34]  Todd A. Kuiken,et al.  Frequency- and time-domain FEM models of EMG: capacitive effects and aspects of dispersion , 2002, IEEE Transactions on Biomedical Engineering.

[35]  N. Dimitrova,et al.  Precise and fast calculation of the motor unit potentials detected by a point and rectangular plate electrode. , 1998, Medical engineering & physics.

[36]  Serge H. Roy,et al.  Modeling of surface myoelectric signals. II. Model-based signal interpretation , 1999, IEEE Transactions on Biomedical Engineering.