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A study of the motor unit potential for application to the automatic analysis of clinical EMG signals

University of British Columbia

A computer model of the human single motor unit potential has been created for the purpose of developing methods of automated analysis in clinical electromyography. This approach was taken in order to examine the effects of pathological changes on the electromyographic potentials. A comprehensive review of the previous methods of automatic analysis of clinical EMG signals described in the literature has been presented and discussed, together with the relevant work on the production and detection of electrical activity with intramuscular electrodes. A methodology has been devised for the collection and preprocessing of the electromyographic signals and an . EMG data base established at U.B.C. An interactive graphics routine was developed to display the EMG waveform and allow the extraction of single motor unit potentials for further analysis. A computer model has been proposed for the generation of single motor unit potentials observed during clinical EMG examinations of the normal biceps brachii muscle. This model was based on physiological findings. In the model the single fiber activity was represented by a dipole current source and the motor unit was constructed from a uniform random array of fibers. Motor unit potentials generated from this array were examined at various points both inside and outside the array and the effects of single fiber axial dispersion, were investigated. The simulated motor unit potentials generated by the model have been compared with existing data from multielectrode studies in biceps brachii. The hypothesis that there is a variation in motor unit potential shape at successive discharges was investigated and the model employed for this purpose. It has been shown that for the normal motor unit potential, one major contributor to the shape variance is electromyographic jitter. The predictions from the model were compared with human experimental data. These results reveal that the variance may be a useful diagnostic indicator, although further research is warranted.

Text

2010-02-09

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http://hdl.handle.net/2429/19881

Electrical and Computer Engineering

University of British Columbia

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