A software package for interactive motor unit potential classification using fuzzy k-NN classifier

We present an interactive software package for implementing the supervised classification task during electromyographic (EMG) signal decomposition process using a fuzzy k-NN classifier and utilizing the MATLAB high-level programming language and its interactive environment. The method employs an assertion-based classification that takes into account a combination of motor unit potential (MUP) shapes and two modes of use of motor unit firing pattern information: the passive and the active modes. The developed package consists of several graphical user interfaces used to detect individual MUP waveforms from a raw EMG signal, extract relevant features, and classify the MUPs into motor unit potential trains (MUPTs) using assertion-based classifiers.

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

[2]  J. Basmajian.  Muscles Alive—their functions revealed by electromyography , 1963 .

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

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

[5]  E. Kaplan.  Muscles Alive. Their Functions Revealed by Electromyography. J. V. Basmajian. Baltimore, The Williams and Wilkins Co., 1962. $8.50 , 1962 .

[6]  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.

[7]  David G. Stork,et al.  Pattern Classification , 1973 .

[8]  Ludmila I. Kuncheva,et al.  Fuzzy Classifier Design , 2000, Studies in Fuzziness and Soft Computing.

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

[10]  Daniel W. Stashuk,et al.  Physiologically based simulation of clinical EMG signals , 2005, 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]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[14]  Swarup Medasani,et al.  An overview of membership function generation techniques for pattern recognition , 1998, Int. J. Approx. Reason..

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

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

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

[18]  Anil K. Jain,et al.  Classifier design with Parzen Windows , 1988 .

[19]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

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

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

[22]  Ch Chen,et al.  Pattern recognition and artificial intelligence , 1976 .

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

[24]  A. Jówik,et al.  A learning scheme for a fuzzy k-NN rule , 1983 .