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Evaluation of an Automated Method for Analysing the Electromyogram

Published online by Cambridge University Press:  18 September 2015

R.E.P. Sica
Affiliation:
Medical Research Council Group in Developmental Neurobiology, McMaster University, Hamilton, Ontario, Canada
A.J. McComas*
Affiliation:
Medical Research Council Group in Developmental Neurobiology, McMaster University, Hamilton, Ontario, Canada
J.C.D. Ferreira
Affiliation:
Medical Research Council Group in Developmental Neurobiology, McMaster University, Hamilton, Ontario, Canada
*
Department of Neurology, Room 4U7, McMaster University Medical Centre, 1200 Main Street West, Hamilton, Ontario, Canada, L8S 4J9.
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An automated system, incorporating the ANOPS-101 mini-computer, has been used to analyse the EMG. The vastus medialis (VM) and biceps brachii (BB) muscles were studied in 28 controls, 16 patients with myopathies, and in 26 patients with denervating disorders. For each muscle mean values were computed for the durations and numbers of phases of muscle action potentials; the mean density and amplitude of the deflections in the interference pattern were also measured. A higher incidence of abnormalities could be detected in myopathic than in neuropathic disorders; for both conditions the incidence was significantly greater in BB than in VM. For the diagnosis of denervation the most useful measurement was that of potential duration; for the detection of myopathies amplitude determinations were also very useful. The present results have been compared with those of other published studies in which automatic EMG analysis has been employed.

Type
Research Article
Copyright
Copyright © Canadian Neurological Sciences Federation 1978

References

REFERENCES

Buchthal, F. (1957). An Introduction to Electromyography. Oslo: Scandinavian University Books.Google Scholar
Campbell, M.J., McComas, A.J. and Petito, F. (1973). Physiological changes in ageing muscles. Journal of Neurology, Neurosurgery and Psychiatry, 36, 174182.CrossRefGoogle ScholarPubMed
Fuglsang-Frederiksen, A. and Mansson, A. (1975). Analysis of electrical activity of normal muscle in man at different degrees of voluntary effort. Journal of Neurology, Neurosurgery and Psychiatry, 38, 683694.CrossRefGoogle Scholar
Fuglsang-Frederiksen, A., Scheel, U. and Buchthal, F. (1976). Diagnostic yield of analysis of the pattern of electrical activity and of individual motor unit potentials in myopathy. Journal of Neurology, Neurosurgery and Psychiatry, 39, 742750.CrossRefGoogle ScholarPubMed
Fuglsang-Frederiksen, A., Scheel, U. and Buchthal, F. (1977). Diagnostic yield of the analysis of the pattern of electrical activity of muscle and of individual motor unit potentials in neurogenic involvement. Journal of Neurology, Neurosurgery and Psychiatry, 40, 544554.CrossRefGoogle ScholarPubMed
Hayward, M. and Willison, R.G. (1973). The recognition of myogenic and neurogenic lesions by quantitative EMG. In New Developments in Electromyography and Clinical Neurophysiology, ed. Desmedt, J.E., vol. 2, pp. 448453. Basel: Karger.Google Scholar
Hayward, M. and Willison, R.G. (1977). Automatic analysis of the electromyogram in patients with chronic partial denervation. Journal of the Neurological Sciences, 33, 415423.CrossRefGoogle ScholarPubMed
Kopec, J. and Hausmanowa-Petrusewicz, I. (1976). On-line computer application in clinical quantitative electromyography. Electromyography and Clinical Neurophysiology, 16, 4964.Google ScholarPubMed
Kopec, J., Hausmanowa-Petrusewicz, I., Rawski, M. and Wolynski, M. (1973). Automatic analysis in electromyography. In New Developments in Electromyography and Clinical Neurophysiology, ed. Desmedt, J.E., vol. 2, pp. 447481. Basel: Karger.Google Scholar
Kunze, K. (1973). Quantitative EMG analysis in myogenic and neurogenic muscle diseases. In New Developments in Electromyography and Clinical Neurophysiology, ed. Desmedt, J.E., vol. 2, pp. 469476. Basel: Karger.Google Scholar
Lee, R.G. and White, D.G. (1973). Computer analysis of motor unit action potentials in routine clinical electromyography. In New Developments in Electromyography and Clinical Neurophysiology, ed. Desmedt, J.E., vol. 2, pp. 454461. Basel: Karger.Google Scholar
McComas, A.J. and Sica, R.E.P. (1978). Automatic quantitative analysis of the electromyogram in partially denervated distal muscles: a comparison with motor unit counting. Canadian Journal of Neurological Sciences. In press.CrossRefGoogle ScholarPubMed
McComas, A.J., Sica, R.E.P. and Campbell, M.J. (1971). ‘Sick’ motoneurones. A unifying concept of muscle disease. Lancet, 1. 321325.CrossRefGoogle ScholarPubMed
Rose, A.L. and Willison, R.G. (1967). Quantitative electromyography using automatic analysis: studies in healthy subjects and patients with primary muscle disease. Journal of Neurology, Neurosurgery and Psychiatry, 30, 403410.CrossRefGoogle ScholarPubMed
Walton, J.N. (1952). The electromyogram in myopathy. Analysis with the audiofrequency spectrometer. Journal of Neurology, Neurosurgery and Psychiatry, 15, 219226.CrossRefGoogle ScholarPubMed
Willison, R.G. (1964). Analysis of electrical activity in healthy and dystrophic muscle in man. Journal of Neurology, Neurosurgery and Psychiatry, 27, 386394.CrossRefGoogle ScholarPubMed