Rigorous a Posteriori Assessment of Accuracy in EMG Decomposition

If electromyography (EMG) decomposition is to be a useful tool for scientific investigation, it is essential to know that the results are accurate. Because of background noise, waveform variability, motor-unit action potential (MUAP) indistinguishability, and perplexing superpositions, accuracy assessment is not straightforward. This paper presents a rigorous statistical method for assessing decomposition accuracy based only on evidence from the signal itself. The method uses statistical decision theory in a Bayesian framework to integrate all the shape- and firing-time-related information in the signal to compute an objective a posteriori measure of confidence in the accuracy of each discharge in the decomposition. The assessment is based on the estimated statistical properties of the MUAPs and noise and takes into account the relative likelihood of every other possible decomposition. The method was tested on 3 pairs of real EMG signals containing 4-7 active MUAP trains per signal that had been decomposed by a human expert. It rated 97% of the identified MUAP discharges as accurate to within ± 0.5 ms with a confidence level of 99%, and detected six decomposition errors. Cross-checking between signal pairs verified all but two of these assertions. These results demonstrate that the approach is reliable and practical for real EMG signals.

[1]  Alexander Adam,et al.  Recruitment order of motor units in human vastus lateralis muscle is maintained during fatiguing contractions. , 2003, Journal of neurophysiology.

[2]  K. S. Gaard,et al.  Motor unit recruitment pattern during low level static and dynamic contractions , 1995 .

[3]  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.

[4]  E. Fogel,et al.  Spectral analysis of previewing controllers , 1980 .

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

[6]  G. Kamen,et al.  Independence of motor unit recruitment and rate modulation during precision force control , 1999, Neuroscience.

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

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

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

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

[11]  D. Zazula,et al.  Correlation-based decomposition of surface electromyograms at low contraction forces , 2004, Medical and Biological Engineering and Computing.

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

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

[14]  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.

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

[16]  D. Farina,et al.  Non-uniform adaptation of motor unit discharge rates during sustained static contraction of the upper trapezius muscle , 2008, Experimental Brain Research.

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

[18]  Stephan Riek,et al.  Recruitment and rate coding organisation for soleus motor units across entire range of voluntary isometric plantar flexions , 2009, The Journal of physiology.

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

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

[21]  Kevin C. McGill,et al.  Automatic decomposition of the electromyogram , 1983 .

[22]  G. P. Moore,et al.  Neuronal spike trains and stochastic point processes. I. The single spike train. , 1967, Biophysical journal.

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

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

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

[26]  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.

[27]  G Kamen,et al.  Superficial motor units are larger than deeper motor units in human vastus lateralis muscle , 2005, Muscle & nerve.

[28]  Christian P. Robert,et al.  Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.

[29]  G Kamen,et al.  Adaptations in maximal motor unit discharge rate to strength training in young and older adults , 2001, Muscle & nerve.

[30]  G. P. Moore,et al.  Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. , 1967, Biophysical journal.

[31]  C J De Luca,et al.  Motor control of low-threshold motor units in the human trapezius muscle. , 2001, Journal of neurophysiology.

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

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

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

[35]  Bert U Kleine,et al.  Using two-dimensional spatial information in decomposition of surface EMG signals. , 2007, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

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

[37]  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.

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

[39]  Stephan Riek,et al.  Motor Unit Recruitment Strategies Are Altered during Deep-Tissue Pain , 2009, The Journal of Neuroscience.

[40]  W. J. Langford.  Statistical Methods , 1959, Nature.

[41]  C J De Luca,et al.  Rank‐ordered regulation of motor units , 1996, Muscle & nerve.