A method to test reliability and accuracy of the decomposition of multi-channel long-term intramuscular EMG signal recordings

Abstract More and more research groups use intramuscular electromyogram (EMG) recordings and there is a need for evaluating decomposed signals with respect to reliability and accuracy. We propose and tested a method to evaluate reliability and accuracy of the decomposition procedure of real, multi-channel long-term EMG signals. The method is based on a comparison of the decomposition results of the original signals with the firing distribution of the rotated signals. The rotation is achieved by reversing the original signal so that the last sample becomes the first and the first sample the last. A number of performance parameters were defined to compare the signals. The method was applied on 76 intramuscular long-term EMG signals that were automatically decomposed by our decomposition software (EMG-LODEC). The EMG signals contained between 0 and 37923 motor unit action potentials. The method enabled us to test the quality of the decomposition results of intramuscular signals and to scrutinize the non-reliable part of the decomposition to ensure that the results were accurate. Achieved reliability was good in approximately 50% of the recordings. Only four were clearly insufficient. Relevance to industry Work-related shoulder-neck pain is a major health risk in computer operators. To understand the physiological mechanisms behind the development of these disorders, EMG recordings of some minutes up to several hours must be accurately decomposed. This paper presents a method to test reliability and accuracy of the decomposition procedure of measured intramuscular signals of long duration. The method seems to be suitable for evaluating decomposition results of measured EMG signals and constitutes a useful tool for EMG researchers to test their intramuscular recordings.

[1]  M. Hagberg,et al.  Prevalence rates and odds ratios of shoulder-neck diseases in different occupational groups. , 1987, British journal of industrial medicine.

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

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

[4]  Steen Andreassen,et al.  Computer-Aided Electromyography and Expert Systems , 1989 .

[5]  L Finsen,et al.  Motor unit activity during stereotyped finger tasks and computer mouse work. , 2001, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

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

[7]  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.

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

[9]  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.

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

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

[12]  Roland Kadefors,et al.  Motor-unit recruitment in the trapezius muscle during arm movements and in VDU precision work , 1999 .

[13]  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.

[14]  S. Siegel,et al.  Nonparametric Statistics for the Behavioral Sciences , 2022, The SAGE Encyclopedia of Research Design.

[15]  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.

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

[17]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

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

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

[20]  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.

[21]  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.

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

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

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

[25]  O. Heinisch,et al.  Pearce, S. C.: Biological Statistics, an Introduction. Mc‐Graw Hill Book Company, New York, London 1965. XIII + 212 S., Preis $ 9,50 , 1967 .