Evaluation of intra-muscular EMG signal decomposition algorithms.

We propose and test a tool to evaluate and compare EMG signal decomposition algorithms. A model for the generation of synthetic intra-muscular EMG signals, previously described, has been used to obtain reference decomposition results. In order to evaluate the performance of decomposition algorithms it is necessary to define indexes which give a compact but complete indication about the quality of the decomposition. The indexes given by traditional detection theory are in this paper adapted to the multi-class EMG problem. Moreover, indexes related to model parameters are also introduced. It is possible in this way to compare the sensitivity of an algorithm to different signal features. An example application of the technique is presented by comparing the results obtained from a set of synthetic signals decomposed by expert operators having no information about the signal features using two different algorithms. The technique seems to be appropriate for evaluating decomposition performance and constitutes a useful tool for EMG signal researchers to identify the algorithm most appropriate for their needs.

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

[2]  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.

[3]  AAEE glossary of terms in clinical electromyography. , 1987, Muscle & nerve.

[4]  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).

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

[6]  James P. Egan,et al.  Signal detection theory and ROC analysis , 1975 .

[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]  Erik Stålberg,et al.  Multi-MUP EMG analysis - a two year experience with a quantitative method in daily routine. , 1995 .

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

[10]  Dario Farina,et al.  Evaluation of needle EMG decomposition algorithms with synthetic test signals , 2000 .

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

[12]  E Stålberg,et al.  The ability of MUP parameters to discriminate between normal and neurogenic MUPs in concentric EMG: analysis of the MUP "thickness" and the proposal of "size index". , 1993, Electroencephalography and clinical neurophysiology.

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

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

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

[16]  Timothy John Doherty Age-related Changes In The Numbers And Physiological Properties Of Human Motor Units , 1993 .

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

[18]  R Kadefors,et al.  Recruitment of low threshold motor-units in the trapezius muscle in different static arm positions. , 1999, Ergonomics.