A novel method for automated EMG decomposition and MUAP classification

OBJECTIVE This paper proposes a novel method for the extraction and classification of individual motor unit action potentials (MUAPs) from intramuscular electromyographic signals. METHODOLOGY The proposed method automatically detects the number of template MUAP clusters and classifies them into normal, neuropathic or myopathic. It consists of three steps: (i) preprocessing of electromyogram (EMG) recordings, (ii) MUAP detection and clustering and (iii) MUAP classification. RESULTS The approach has been validated using a dataset of EMG recordings and an annotated collection of MUAPs. The correct identification rate for MUAP clustering is 93, 95 and 92% for normal, myopathic and neuropathic, respectively. Ninety-one percent of the superimposed MUAPs were correctly identified. The obtained accuracy for MUAP classification is about 86%. CONCLUSION The proposed method, apart from efficient EMG decomposition addresses automatic MUAP classification to neuropathic, myopathic or normal classes directly from raw EMG signals.

[1]  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.

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

[3]  J Yang,et al.  [Extraction of MUAP from NEMG signal using self-organization competing NN]. , 2001, Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi.

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

[5]  Fritz Buchthal,et al.  An introduction to electromyography , 1957 .

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

[7]  S. Sathiya Keerthi,et al.  Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.

[8]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

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

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

[11]  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.

[12]  M. Brooke,et al.  Myology , 1987, Neurology.

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

[14]  S Andreassen,et al.  Methods for computer-aided measurement of motor unit parameters. , 1987, Electroencephalography and clinical neurophysiology. Supplement.

[15]  Christian Krarup,et al.  Pitfalls in electrodiagnosis , 1999, Journal of Neurology.

[16]  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.

[17]  J. Platt.  Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

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

[19]  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.

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

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

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

[23]  Gérard Dreyfus,et al.  Single-layer learning revisited: a stepwise procedure for building and training a neural network , 1989, NATO Neurocomputing.

[24]  James W. Albers,et al.  Review of Quantitative and Automated Needle Electromyographic Analyses , 1981, IEEE Transactions on Biomedical Engineering.

[25]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[26]  C. C. Chang,et al.  Libsvm : introduction and benchmarks , 2000 .

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

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

[29]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

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

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

[32]  F BUCHTHAL,et al.  Action potential parameters in normal human muscle and their dependence on physical variables. , 1954, Acta physiologica Scandinavica.