Real time estimation of generation, extinction and flow of muscle fibre action potentials in high density surface EMG

PURPOSE Developing a real time method to estimate generation, extinction and propagation of muscle fibre action potentials from bi-dimensional and high density surface electromyogram (EMG). METHOD A multi-frame generalization of an optical flow technique including a source term is considered. A model describing generation, extinction and propagation of action potentials is fit to epochs of surface EMG. RESULTS The algorithm is tested on simulations of high density surface EMG (inter-electrode distance equal to 5mm) from finite length fibres generated using a multi-layer volume conductor model. The flow and source term estimated from interference EMG reflect the anatomy of the muscle, i.e. the direction of the fibres (2° of average estimation error) and the positions of innervation zone and tendons under the electrode grid (mean errors of about 1 and 2mm, respectively). The global conduction velocity of the action potentials from motor units under the detection system is also obtained from the estimated flow. The processing time is about 1 ms per channel for an epoch of EMG of duration 150 ms. CONCLUSIONS A new real time image processing algorithm is proposed to investigate muscle anatomy and activity. Potential applications are proposed in prosthesis control, automatic detection of optimal channels for EMG index extraction and biofeedback.

[1]  Guruprasad Madhavan,et al.  Electromyography: Physiology, Engineering and Non-Invasive Applications , 2005, Annals of Biomedical Engineering.

[2]  Michal Irani,et al.  Multi-frame optical flow estimation using subspace constraints , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[3]  Luca Mesin Simulation of Surface EMG Signals for a Multilayer Volume Conductor With Triangular Model of the Muscle Tissue , 2006, IEEE Transactions on Biomedical Engineering.

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

[5]  Ruzena Bajcsy,et al.  Advances in Computer Vision , 1997, Advances in Computing Science.

[6]  Abdulhamit Subasi,et al.  Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders , 2013, Comput. Biol. Medicine.

[7]  N. Ostlund,et al.  Location of innervation zone determined with multichannel surface electromyography using an optical flow technique. , 2007, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[8]  M. Samet,et al.  Parametric study on the dielectric properties of biological tissues , 2015, 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA).

[9]  Roberto Merletti,et al.  Separation of propagating and non propagating components in surface EMG , 2008, Biomed. Signal Process. Control..

[10]  D. Winter,et al.  Models of recruitment and rate coding organization in motor-unit pools. , 1993, Journal of neurophysiology.

[11]  Nils Östlund,et al.  Simultaneous estimation of muscle fibre conduction velocity and muscle fibre orientation using 2D multichannel surface electromyogram , 2006, Medical and Biological Engineering and Computing.

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

[13]  Luca Mesin,et al.  Volume conductor models in surface electromyography: Computational techniques , 2013, Comput. Biol. Medicine.

[14]  Gea Drost,et al.  Clinical applications of high-density surface EMG: a systematic review. , 2006, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[15]  M. Ruiz Espejo Sampling , 2013, Encyclopedic Dictionary of Archaeology.

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

[17]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[18]  R. Enoka,et al.  Detecting the unique representation of motor-unit action potentials in the surface electromyogram. , 2008, Journal of neurophysiology.

[19]  Hisashi Kawai,et al.  Investigation of Innervation Zone Shift with Continuous Dynamic Muscle Contraction , 2013, Comput. Math. Methods Medicine.

[20]  V. Dietz,et al.  Providing the clinical basis for new interventional therapies: refined diagnosis and assessment of recovery after spinal cord injury , 2004, Spinal Cord.

[21]  A. Beardwell Electromyography , 1945 .

[22]  R. LeVeque Finite Difference Methods for Ordinary and Partial Differential Equations: Steady-State and Time-Dependent Problems (Classics in Applied Mathematics Classics in Applied Mathemat) , 2007 .

[23]  Heidrun Wabnitz,et al.  Cerebral Perfusion in Acute Stroke Monitored by Time-domain Near-infrared Reflectometry , 2012 .

[24]  Harri Hohti,et al.  Optical flow in radar images , 2004 .

[25]  Roberto Merletti,et al.  Advances in surface EMG: recent progress in detection and processing techniques. , 2010, Critical reviews in biomedical engineering.

[26]  Dario Farina,et al.  A finite element model for describing the effect of muscle shortening on surface EMG , 2006, IEEE Transactions on Biomedical Engineering.

[27]  Neill E. Bowler,et al.  Development of a precipitation nowcasting algorithm based upon optical flow techniques , 2004 .

[28]  MesinLuca Volume conductor models in surface electromyography , 2013 .

[29]  J. Limb,et al.  Estimating the Velocity of Moving Images in Television Signals , 1975 .

[30]  Roberto Merletti,et al.  Insights gained into the interpretation of surface electromyograms from the gastrocnemius muscles: A simulation study. , 2011, Journal of biomechanics.

[31]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[32]  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.

[33]  Luca Mesin,et al.  Short range tracking of rainy clouds by multi-image flow processing of X-band radar data , 2011, EURASIP J. Adv. Signal Process..

[34]  F Stegeman Dick,et al.  High-density Surface EMG: Techniques and Applications at a Motor Unit Level , 2012 .

[35]  David G. Lloyd,et al.  A real-time EMG-driven virtual arm , 2002, Comput. Biol. Medicine.

[36]  Roberto Merletti,et al.  Assessment of force and fatigue in isometric contractions of the upper trapezius muscle by surface EMG signal and perceived exertion scale. , 2008, Gait & posture.

[37]  Roberto Merletti,et al.  A bi-dimensional index for the selective assessment of myoelectric manifestations of peripheral and central muscle fatigue. , 2009, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[38]  David J. Fleet,et al.  Optical Flow Estimation , 2006, Handbook of Mathematical Models in Computer Vision.

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

[40]  Jamileh Yousefi,et al.  Characterizing EMG data using machine-learning tools , 2014, Comput. Biol. Medicine.

[41]  Roberto Merletti,et al.  Advances in surface EMG: recent progress in clinical research applications. , 2010, Critical reviews in biomedical engineering.

[42]  Luca Mesin Analytical Generation Model of Surface Electromyogram for Multi Layer Volume Conductors , 2005 .

[43]  Dario Farina,et al.  An analytical model for surface EMG generation in volume conductors with smooth conductivity variations , 2006, IEEE Transactions on Biomedical Engineering.

[44]  R Merletti,et al.  Surface EMG: the issue of electrode location. , 2009, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[45]  Steven S. Beauchemin,et al.  The computation of optical flow , 1995, CSUR.

[46]  Luca Mesin Volume conductor models in surface electromyography: Applications to signal interpretation and algorithm test , 2013, Comput. Biol. Medicine.

[47]  S. Andreassen,et al.  Muscle fibre conduction velocity in motor units of the human anterior tibial muscle: a new size principle parameter. , 1987, The Journal of physiology.

[48]  Dario Farina,et al.  Interpretation of the Surface Electromyogram in Dynamic Contractions , 2006, Exercise and sport sciences reviews.

[49]  R. Scott,et al.  Myoelectric control of prostheses. , 1986, Critical reviews in biomedical engineering.

[50]  Kuo-Chin Fan,et al.  Estimating Optical Flow by Integrating Multi-Frame Information , 2008, J. Inf. Sci. Eng..

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

[52]  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.

[53]  Dario Farina,et al.  Simulation of surface EMG signals generated by muscle tissues with inhomogeneity due to fiber pinnation , 2004, IEEE Transactions on Biomedical Engineering.

[54]  Javaan Chahl,et al.  Biologically inspired visual sensing and flight Control , 2003, The Aeronautical Journal (1968).

[55]  Luca Mesin,et al.  Estimation of monopolar signals from sphincter muscles and removal of common mode interference , 2009, Biomed. Signal Process. Control..

[56]  Roberto Merletti,et al.  Geometry assessment of anal sphincter muscle based on monopolar multichannel surface EMG signals. , 2011, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[57]  D. MacIsaac,et al.  Innervation zone shift with changes in joint angle in the brachial biceps. , 2006, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[58]  Jason M. DeFreitas,et al.  An examination of innervation zone movement with increases in isometric torque production , 2008, Clinical Neurophysiology.

[59]  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.

[60]  P. Komi,et al.  Innervation zone shift at different levels of isometric contraction in the biceps brachii muscle. , 2009, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.