EMG Signal Decomposition Using Motor Unit Potential Train Validity

A system to resolve an intramuscular electromyographic (EMG) signal into its component motor unit potential trains (MUPTs) is presented. The system is intended mainly for clinical applications where several physiological parameters of motor units (MUs), such as their motor unit potential (MUP) templates and mean firing rates, are of interest. The system filters an EMG signal, detects MUPs, and clusters and classifies the detected MUPs into MUPTs. Clustering is partially based on the K-means algorithm, and the supervised classification is implemented using a certainty-based algorithm. Both clustering and supervised classification algorithms use MUP shape and MU firing pattern information along with signal dependent assignment criteria to obtain robust performance across a variety of EMG signals. During classification, the validity of extracted MUPTs are determined using several supervised classifiers; invalid trains are corrected and the assignment threshold for each train is adjusted based on the estimated validity (i.e., adaptive classification). Performance of the developed system in terms of accuracy (Ae), assignment rate (Ar), correct classification rate (CCr), and the error in estimating the number of MUPTs represented in the set of detected MUPs (ENMUPTs) was evaluated using 32 simulated and 30 real EMG signals comprised of 3-11 and 3-15 MUPTs, respectively. The developed system, with average CCr of 86.4% for simulated and 96.4% for real data, outperformed a previously developed EMG decomposition system, with average CCr of 71.6% and 89.7% for simulated and real data, by 14.7% and 6.7%, respectively. In terms of ENMUPTs, the new system, with average ENMUPTs of 0.3 and 0.2 for simulated and real data respectively, was better able to estimate the number of MUPTs represented in a set of detected MUPs than the previous system, with average ENMUPTs of 2.2 and 0.8 for simulated and real data respectively. For both the simulated and real data used, variations in Ac, Ar, and ENMUPTs for the newly developed system were lower than for the previous system, which demonstrates that the new system can successfully adjust the assignment criteria based on the characteristics of a given signal to achieve robust performance across a wide variety of EMG signals, which is of paramount importance for successfully promoting the clinical application of EMG signal decomposition techniques.

[1]  Sanjeev R. Kulkarni,et al.  Learning Pattern Classification - A Survey , 1998, IEEE Trans. Inf. Theory.

[2]  Zhizhong Wang,et al.  Noise reduction based on ICA decomposition and wavelet transform for the extraction of motor unit action potentials , 2006, Journal of Neuroscience Methods.

[3]  P. Matthews.  Relationship of firing intervals of human motor units to the trajectory of post‐spike after‐hyperpolarization and synaptic noise. , 1996, The Journal of physiology.

[4]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[5]  George S. Moschytz,et al.  High-precision EMG signal decomposition using communication techniques , 2000, IEEE Trans. Signal Process..

[6]  C. Moritz,et al.  Discharge rate variability influences the variation in force fluctuations across the working range of a hand muscle. , 2005, Journal of neurophysiology.

[7]  Mohamed S. Kamel,et al.  A Hybrid Classifier Fusion Approach for Motor Unit Potential Classification During EMG Signal Decomposition , 2007, IEEE Transactions on Biomedical Engineering.

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

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

[10]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[11]  Daniel W. Stashuk,et al.  Pattern Classification Techniques for EMG Signal Decomposition , 2009 .

[12]  Hossein Parsaei,et al.  A review of clinical quantitative electromyography. , 2010, Critical reviews in biomedical engineering.

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

[14]  Daniel W Stashuk,et al.  Decomposition‐based quantitative electromyography: Effect of force on motor unit potentials and motor unit number estimates , 2005, Muscle & nerve.

[15]  M. Yoshida,et al.  Application of Independent Component Analysis and Expansion Matching Filter for Automatic Detection of Motor Unit Action Potential Trains , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[16]  Roberto Merletti,et al.  Biophysics of the Generation of EMG Signals , 2004 .

[17]  D. Stashuk,et al.  Decomposition‐based quantitative electromyography: Methods and initial normative data in five muscles , 2003, Muscle & nerve.

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

[19]  J. Ushiba,et al.  Decomposition of electromyographic signal by principal component analysis of wavelet coefficients , 2003, IEEE EMBS Asian-Pacific Conference on Biomedical Engineering, 2003..

[20]  Victor Lesser,et al.  Integrated processing and understanding of signals , 1992 .

[21]  Constantinos S. Pattichis,et al.  Neural network models in EMG diagnosis , 1995 .

[22]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[23]  Kun He.  Automated Measurement of Neuromuscular Jitter Based on EMG Signal Decomposition , 2007 .

[24]  Dario Farina,et al.  Analysis of intramuscular electromyogram signals , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[25]  Victor R. Lesser,et al.  IPUS: An Architecture for the Integrated Processing and Understanding of Signals , 1995, Artif. Intell..

[26]  John E. Desmedt,et al.  Computer-aided electromyography , 1983 .

[27]  Carlo J De Luca,et al.  Decomposition of indwelling EMG signals. , 2008, Journal of applied physiology.

[28]  Daniel W. Stashuk,et al.  Physiologically based simulation of clinical EMG signals , 2005, IEEE Transactions on Biomedical Engineering.

[29]  S. Andreassen,et al.  Quantitative Analysis of Individual Motor Unit Potentials: A Proposition for Standardized Terminology and Criteria for Measurement , 1986, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[30]  R Merletti,et al.  Evaluation of intra-muscular EMG signal decomposition algorithms. , 2001, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[31]  Hossein Parsaei,et al.  An SVM classifier for detecting merged motor unit potential trains extracted by EMG signal decomposition using their MUP shape information , 2011, 2011 24th Canadian Conference on Electrical and Computer Engineering(CCECE).

[32]  C.E. Shannon,et al.  Communication in the Presence of Noise , 1949, Proceedings of the IRE.

[33]  Moh'd Belal Al-Zoubi,et al.  New outlier detection method based on fuzzy clustering , 2010 .

[34]  S D Nandedkar,et al.  Multi‐motor unit action potential analysis (MMA) , 1995, Muscle & nerve.

[35]  L J Dorfman,et al.  Motor unit firing rates and firing rate variability in the detection of neuromuscular disorders. , 1989, Electroencephalography and clinical neurophysiology.

[36]  Alexander Adam,et al.  Motor unit control and force fluctuation during fatigue. , 2009, Journal of applied physiology.

[37]  George S. Moschytz,et al.  A New Framework and Computer Program for Quantitative EMG Signal Analysis , 1984, IEEE Transactions on Biomedical Engineering.

[38]  G.S. Moschytz,et al.  Decomposition of electromyogram long-term recordings , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[39]  Luigi di Stefano,et al.  Fast template matching using bounded partial correlation , 2003, Machine Vision and Applications.

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

[41]  Shiro Usui,et al.  Digital Low-Pass Differentiation for Biological Signal Processing , 1982, IEEE Transactions on Biomedical Engineering.

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

[43]  George S. Moschytz,et al.  A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients , 2003, IEEE Transactions on Biomedical Engineering.

[44]  Peter J. Kyberd,et al.  EMG signal filtering based on Empirical Mode Decomposition , 2006, Biomed. Signal Process. Control..

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

[46]  Douglas Steinley,et al.  K-means clustering: a half-century synthesis. , 2006, The British journal of mathematical and statistical psychology.

[47]  Mohamed S. Kamel,et al.  An interactive environment for motor unit potential classification using certainty-based classifiers , 2008, Simul. Model. Pract. Theory.

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

[49]  O. Fokapu,et al.  Automatic identification of motor unit action potential trains from electromyographic signals using fuzzy techniques , 2003, Medical and Biological Engineering and Computing.

[50]  R. Okuno,et al.  A decomposition algorithm for surface electrode-array electromyogram , 2005, IEEE Engineering in Medicine and Biology Magazine.

[51]  J. Fang,et al.  Decomposition of multiunit electromyographic signals , 1999, IEEE Transactions on Biomedical Engineering.

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

[53]  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).

[54]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[55]  D B Sanders,et al.  AAEM minimonograph #25: Single‐fiber electromyography , 1996, Muscle & nerve.

[56]  Hossein Parsaei,et al.  SVM-Based Validation of Motor Unit Potential Trains Extracted by EMG Signal Decomposition , 2012, IEEE Transactions on Biomedical Engineering.

[57]  R. Wotiz,et al.  Improved resolution of pulse superpositions in a knowledge-based system EMG decomposition , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[58]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[59]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[60]  Roberto Merletti,et al.  Decomposition of Intramuscular EMG Signals , 2004 .

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

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

[63]  Dimitrios I. Fotiadis,et al.  Automatic discovery of the number of MUAP clusters and superimposed MUAP decomposition in electromyograms , 2003, 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003..

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

[65]  W. Boron,et al.  Medical physiology : a cellular and molecular approach , 2002 .

[66]  M. Z. Kermani,et al.  Estimation of recruitment and firing rate in EMG signals , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[67]  D. Stashuk,et al.  Automatic decomposition of selective needle-detected myoelectric signals , 1988, IEEE Transactions on Biomedical Engineering.

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

[69]  Dario Farina,et al.  A new method for the extraction and classification of single motor unit action potentials from surface EMG signals , 2004, Journal of Neuroscience Methods.

[70]  Rui J. P. de Figueiredo,et al.  Separation of superimposed signals by a cross-correlation method , 1982, ICASSP.

[71]  Damjan Zazula,et al.  Computer-assisted decomposition of the electromyograms , 1998, Proceedings. 11th IEEE Symposium on Computer-Based Medical Systems (Cat. No.98CB36237).

[72]  R Dengler,et al.  The role of electromyography (EMG) in the diagnosis of ALS. , 2000, Amyotrophic lateral sclerosis and other motor neuron disorders : official publication of the World Federation of Neurology, Research Group on Motor Neuron Diseases.

[73]  E Stålberg,et al.  Macro EMG, a new recording technique. , 1980, Journal of neurology, neurosurgery, and psychiatry.

[74]  Sotiris B. Kotsiantis,et al.  Machine learning: a review of classification and combining techniques , 2006, Artificial Intelligence Review.

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

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

[77]  B Mambrito,et al.  A technique for the detection, decomposition and analysis of the EMG signal. , 1984, Electroencephalography and clinical neurophysiology.

[78]  S. Podnar,et al.  Bayesian characterization of external anal sphincter muscles using quantitative electromyography , 2008, Clinical Neurophysiology.

[79]  Hossein Parsaei,et al.  Intramuscular EMG signal decomposition. , 2010, Critical reviews in biomedical engineering.

[80]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

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

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

[83]  Daniel W. Stashuk,et al.  Clinical Characterization of Electromyographic Data Using Computational Tools , 2006, 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology.

[84]  George S. Moschytz,et al.  Decomposition of EMG signals using time-frequency features , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[85]  G. R. Davis,et al.  Jitter correction: A computer algorithm for reduction of the velocity recovery function artifact , 1988, Muscle & nerve.

[86]  Armando Malanda-Trigueros,et al.  Automated decomposition of intramuscular electromyographic signals , 2006, IEEE Transactions on Biomedical Engineering.

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

[88]  H. M. Russell,et al.  Muscles Alive: Their Functions Revealed by Electromyography 3rd ed , 1974 .

[89]  M.H. Hassoun,et al.  NNERVE: Neural Network Extraction of Repetitive Vectors for Electromyography. II. Performance analysis , 1994, IEEE Transactions on Biomedical Engineering.

[90]  Hossein Parsaei,et al.  Validating motor unit firing patterns extracted by EMG signal decomposition , 2011, Medical & Biological Engineering & Computing.

[91]  Kevin C. McGill,et al.  Optimal resolution of superimposed action potentials , 2002, IEEE Transactions on Biomedical Engineering.

[92]  Hans-Andrea Loeliger,et al.  Resolution of Superpositions in EMG Signals using Belief Propagation: Results for the Known Constituent Problem , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[93]  C. D. De Luca.  Physiology and Mathematics of Myoelectric Signals , 1979, IEEE Transactions on Biomedical Engineering.

[94]  Håkan Johansson,et al.  Modern Techniques in Neuroscience Research , 1999, Springer Berlin Heidelberg.

[95]  Thomas Läubli,et al.  A method to test reliability and accuracy of the decomposition of multi-channel long-term intramuscular EMG signal recordings , 2002 .

[96]  J. Hannerz.  An electrode for recording single motor unit activity during strong muscle contractions. , 1974, Electroencephalography and clinical neurophysiology.

[97]  Ying Liu,et al.  Cluster-based outlier detection , 2009, Ann. Oper. Res..

[98]  Zhizhong Wang,et al.  MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition , 2006, Medical and Biological Engineering and Computing.

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

[100]  Richard Bellman,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

[101]  A R Spitzer,et al.  Assessment of variability in the shape of the motor unit action potential, "the jiggle," at consecutive discharges. , 1995, Muscle & nerve.

[102]  D W Stashuk,et al.  Motor unit potential characterization using "pattern discovery". , 2008, Medical engineering & physics.

[103]  Miki Nikolic,et al.  EMGTools, an Adaptive and Versatile Tool for Detailed EMG Analysis , 2011, IEEE Transactions on Biomedical Engineering.

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

[105]  Dario Farina,et al.  A model for the generation of synthetic intramuscular EMG signals to test decomposition algorithms , 2001, IEEE Transactions on Biomedical Engineering.

[106]  K C McGill,et al.  Automatic decomposition of multichannel intramuscular EMG signals. , 2009, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

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

[108]  S. D. Nandedkar,et al.  Median averaging of electromyographic motor unit action potentials: comparison with other techniques , 2006, Medical and Biological Engineering and Computing.

[109]  D. Stashuk,et al.  Supervised mutual-information based feature selection for motor unit action potential classification , 1997, Medical and Biological Engineering and Computing.

[110]  K.C. McGill,et al.  Validation of a computer-aided EMG decomposition method , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[111]  Palma Blonda,et al.  A survey of fuzzy clustering algorithms for pattern recognition. I , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[112]  S.M.P. Firoozabadi,et al.  A New Approach for EMG Decomposition Based on Overlaps Solution , 2007, 2007 15th International Conference on Digital Signal Processing.

[113]  Kevin C. McGill,et al.  EMGLAB: An interactive EMG decomposition program , 2005, Journal of Neuroscience Methods.

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

[115]  H.-A. Loeliger,et al.  Decomposition of electromyographic signals by iterative message passing in a graphical model , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[116]  Christos D. Katsis,et al.  A novel method for automated EMG decomposition and MUAP classification , 2006, Artif. Intell. Medicine.

[117]  S D Nandedkar,et al.  Quantitative EMG in inflammatory myopathy , 1990, Muscle & nerve.

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

[119]  Christos D. Katsis,et al.  A two-stage method for MUAP classification based on EMG decomposition , 2007, Comput. Biol. Medicine.

[120]  Carlo J. De Luca,et al.  Some properties of motor unit action potential trains recorded during constant force isometric contractions in man , 1973, Kybernetik.

[121]  Dario Farina,et al.  Unsupervised Bayesian Decomposition of Multiunit EMG Recordings Using Tabu Search , 2010, IEEE Transactions on Biomedical Engineering.

[122]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[123]  Hossein Parsaei,et al.  Validation of motor unit potential trains using motor unit firing pattern information , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[124]  Paolo Bonato,et al.  Motor unit firing characteristics in patients with amyotrophic lateral sclerosis , 2009, 2009 IEEE 35th Annual Northeast Bioengineering Conference.

[125]  D. Stashuk,et al.  Resolving superimposed motor unit action potentials , 2007, Medical and Biological Engineering and Computing.

[126]  Roberto Merletti,et al.  Needle and Wire Detection Techniques , 2004 .

[127]  K. McGill,et al.  Automatic decomposition electromyography (ADEMG): validation and normative data in brachial biceps. , 1985, Electroencephalography and clinical neurophysiology.

[128]  G.S. Moschytz,et al.  A decomposition software package for the decomposition of long-term multi-channel electromyrographic signals , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[129]  G. S. Moschytz,et al.  Electromyogram decomposition using the single-lineage clustering algorithm and wavelets , 1999, ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357).

[130]  D.W. Stashuk.  Mean, median and mode estimation of motor unit action potential templates , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[131]  K C McGill,et al.  Rigorous a Posteriori Assessment of Accuracy in EMG Decomposition , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[132]  Nasser Ali,et al.  A new digital signal processing technique for the estimation of motor unit action potential templates , 2009, 2009 International Conference on Computer Engineering & Systems.

[133]  Daniel W Stashuk,et al.  Physiological characteristics of motor units in the brachioradialis muscle across fatiguing low-level isometric contractions. , 2008, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[134]  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.

[135]  Sarbast M. Rasheed,et al.  A Multiclassifier Approach to Motor Unit Potential Classification for EMG Signal Decomposition , 2006 .

[136]  Carlo J. De Luca,et al.  Aliasing rejection in Precision Decomposition of EMG signals , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[137]  Palma Blonda,et al.  A survey of fuzzy clustering algorithms for pattern recognition. II , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[138]  Kevin C McGill,et al.  Estimating motor-unit architectural properties by analyzing motor-unit action potential morphology , 2001, Clinical Neurophysiology.

[139]  Joshua C. Kline,et al.  Decomposition of surface EMG signals. , 2006, Journal of neurophysiology.

[140]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[141]  Mohamed S. Kamel,et al.  Integrating Heterogeneous Classifier Ensembles for EMG Signal Decomposition Based on Classifier Agreement , 2010, IEEE Transactions on Information Technology in Biomedicine.

[142]  Hossein Parsaei,et al.  A method for detecting and editing MUPTs contaminated by false classification errors during EMG signal decomposition , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[143]  Mohamed S. Kamel,et al.  Fusion of multiple classifiers for motor unit potential sorting , 2008, Biomed. Signal Process. Control..

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

[145]  Ronald S. Lefever,et al.  A Procedure for Decomposing the Myoelectric Signal Into Its Constituent Action Potentials-Part II: Execution and Test for Accuracy , 1982, IEEE Transactions on Biomedical Engineering.

[146]  D.W. Stashuk,et al.  Probabilistic inference-based classification applied to myoelectric signal decomposition , 1992, IEEE Transactions on Biomedical Engineering.

[147]  André Hardy,et al.  An examination of procedures for determining the number of clusters in a data set , 1994 .

[148]  H P Clamann,et al.  Activity of single motor units during isometric tension , 1970, Neurology.

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

[150]  Mohamed S. Kamel,et al.  Diversity-based combination of non-parametric classifiers for EMG signal decomposition , 2008, Pattern Analysis and Applications.

[151]  Ravi Kothari,et al.  On finding the number of clusters , 1999, Pattern Recognit. Lett..

[152]  C.J. De Luca,et al.  Next-generation decomposition of multi-channel EMG signals , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[153]  D W Stashuk,et al.  Simulation of electromyographic signals. , 1993, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[154]  J.A. Sorensen,et al.  Detailed analysis of motor unit activity , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[155]  Paolo Bonato,et al.  Solving superposition of motor unit action potential waveforms through a cross-time-frequency based methodology , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[156]  Réjean Plamondon,et al.  A Genetic Algorithm for the Resolution of Superimposed Motor Unit Action Potentials , 2007, IEEE Transactions on Biomedical Engineering.

[157]  Hans-Andrea Loeliger,et al.  EMG signal decomposition by loopy belief propagation , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[158]  Robert Tibshirani,et al.  Cluster Validation by Prediction Strength , 2005 .

[159]  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.

[160]  Kevin C. McGill,et al.  Resolving Superimposed MUAPs Using Particle Swarm Optimization , 2009, IEEE Transactions on Biomedical Engineering.

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

[162]  Shaojun Xiao,et al.  Estimation of motor unit firing statistics from surface EMG , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[163]  W. Trojaborg,et al.  The role of quantitative electromyography in inclusion body myositis , 1997, Journal of neurology, neurosurgery, and psychiatry.

[164]  atherine,et al.  Finding the number of clusters in a data set : An information theoretic approach C , 2003 .

[165]  Paolo Bonato,et al.  Decomposition of superimposed waveforms using the cross time frequency transform , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[166]  Jianjun Fang,et al.  Decomposition of EMG signal by wavelet spectrum matching , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[167]  C.J. De Luca,et al.  Multi-Receiver Precision Decomposition of Intramuscular EMG Signals , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[168]  Yang Wang,et al.  Pattern discovery: a data driven approach to decision support , 2003, IEEE Trans. Syst. Man Cybern. Part C.