Validating motor unit firing patterns extracted by EMG signal decomposition

Motor unit (MU) firing pattern information can be used clinically or for physiological investigation. It can also be used to enhance and validate electromyographic (EMG) signal decomposition. However, in all instances the validity of the extracted MU firing patterns must first be determined. Two supervised classifiers that can be used to validate extracted MU firing patterns are proposed. The first classifier, the single/merged classifier (SMC), determines whether a motor unit potential train (MUPT) represents the firings of a single MU or the merged activity of more than one MU. The second classifier, the single/contaminated classifier (SCC), determines whether the estimated number of false-classification errors in a MUPT is acceptable or not. Each classifier was trained using simulated data and tested using simulated and real data. The accuracy of the SMC in categorizing a train correctly is 99% and 96% for simulated and real data, respectively. The accuracy of the SCC is 84% and 81% for simulated and real data, respectively. The composition of these classifiers, their objectives, how they were trained, and the evaluation of their performances using both simulated and real data are presented in detail.

[1]  Dr. D. Stashuk,et al.  Robust supervised classification of motor unit action potentials , 2006, Medical and Biological Engineering and Computing.

[2]  E Stålberg,et al.  The role of electromyography in neurology. , 1997, Electroencephalography and clinical neurophysiology.

[3]  J. Desmedt,et al.  New Developments in Electromyography and Clinical Neurophysiology , 1973 .

[4]  H. Clamann.  Statistical analysis of motor unit firing patterns in a human skeletal muscle. , 1969, Biophysical journal.

[5]  Ronald S. Lefever,et al.  A Procedure for Decomposing the Myoelectric Signal Into Its Constituent Action Potentials - Part I: Technique, Theory, and Implementation , 1982, IEEE Transactions on Biomedical Engineering.

[6]  H. Clamann,et al.  Elsevier/North-Holland Biomedical Press COMPARISON OF THE RECRUITMENT AND DISCHARGE PROPERTIES OF MOTOR UNITS IN H U M A N BRACHIAL BICEPS AND A D D U C T O R POLLICIS D U R I N G ISOMETRIC CONTRACTIONS , 2018 .

[7]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[8]  H. J. Seddon,et al.  New Developments in Electromyography and Clinical Neurophysiology , 1974 .

[9]  D. Stashuk,et al.  Adaptive motor unit action potential clustering using shape and temporal information , 2007, Medical and Biological Engineering and Computing.

[10]  Daniel W. Stashuk,et al.  Fuzzy Classification Using Pattern Discovery , 2007, IEEE Transactions on Fuzzy Systems.

[11]  Mohamed S. Kamel,et al.  Adaptive certainty-based classification for decomposition of EMG signals , 2006, Medical and Biological Engineering and Computing.

[12]  C. Y. Wang,et al.  The null distribution of sample serial correlation coefficient , 2002 .

[13]  Anders Fuglsang-Frederiksen,et al.  The role of different EMG methods in evaluating myopathy , 2006, Clinical Neurophysiology.

[14]  Kevin C. McGill,et al.  Automatic Decomposition of the Clinical Electromyogram , 1985, IEEE Transactions on Biomedical Engineering.

[15]  Yang Wang,et al.  From Association to Classification: Inference Using Weight of Evidence , 2003, IEEE Trans. Knowl. Data Eng..

[16]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[17]  E Stålberg,et al.  Multi-MUP EMG analysis--a two year experience in daily clinical work. , 1995, Electroencephalography and clinical neurophysiology.

[18]  G. H. Loudon,et al.  New signal processing techniques for the decomposition of EMG signals , 1992, Medical and Biological Engineering and Computing.

[19]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[20]  D. Stashuk,et al.  Robust method for estimating motor unit firing-pattern statistics , 2007, Medical and Biological Engineering and Computing.

[21]  C. D. De Luca,et al.  Control scheme governing concurrently active human motor units during voluntary contractions , 1982, The Journal of physiology.

[22]  T Y Sun,et al.  Analysis of motor unit firing patterns in patients with central or peripheral lesions using singular‐value decomposition , 2000, Muscle & nerve.

[23]  E Stalberg,et al.  Calculation of the electromyographic jitter 1 , 1974, Journal of neurology, neurosurgery, and psychiatry.

[24]  D Stashuk,et al.  EMG signal decomposition: how can it be accomplished and used? , 2001, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[25]  C. D. De Luca,et al.  Behaviour of human motor units in different muscles during linearly varying contractions , 1982, The Journal of physiology.

[26]  S Andreassen,et al.  Impaired regulation of force and firing pattern of single motor units in patients with spasticity. , 1980, Journal of neurology, neurosurgery, and psychiatry.

[27]  H. Schoening.  Muscles Alive—Their function revealed by electromyography (5th edition): by John V. Basmajian and Carlo J. Deluca. Hardcover. Price $45.00. 561 pages. Williams and Wilkins, PO Box 969, Waverly Press Lane, Easton, MD 21601 , 1986 .

[28]  C.I. Christodoulou,et al.  Unsupervised pattern recognition for the classification of EMG signals , 1999, IEEE Transactions on Biomedical Engineering.

[29]  Mohamed S. Kamel,et al.  Multi-classification techniques applied to EMG signal decomposition , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[30]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[31]  M.H. Hassoun,et al.  NNERVE: Neural Network Extraction of Repetitive Vectors for Electromyography. I. Algorithm , 1994, IEEE Transactions on Biomedical Engineering.

[32]  Erik Stålberg,et al.  Multi-MUP EMG analysis - a two year experience with a quantitative method in daily routine. , 1995 .

[33]  V. Dietz,et al.  Discharge Characteristics of Single Motor Units in Normal Subjects and Patients with Supraspinal Motor Disturbances1 , 1973 .

[34]  Mohamed Kamel,et al.  Adaptive fuzzy k-NN classifier for EMG signal decomposition. , 2006, Medical engineering & physics.

[35]  Karlheinz Reiners,et al.  Altered mechanisms of muscular force generation in lower motor neuron disease , 1989, Muscle & nerve.

[36]  Charles A. Laszlo,et al.  Estimation of the firing rate of an incomplete MUAP train , 1993, Proceedings of Canadian Conference on Electrical and Computer Engineering.

[37]  B. Falck,et al.  The firing rate of motor units in neuromuscular disorders , 2004, Journal of Neurology.

[38]  Mohamed S. Kamel,et al.  A software package for interactive motor unit potential classification using fuzzy k-NN classifier , 2008, Comput. Methods Programs Biomed..

[39]  Zeynep Erim,et al.  Decomposition of Intramuscular EMG Signals Using a Heuristic Fuzzy Expert System , 2008, IEEE Transactions on Biomedical Engineering.

[40]  D W Stashuk,et al.  Decomposition and quantitative analysis of clinical electromyographic signals. , 1999, Medical engineering & physics.