Automatic Decomposition of the Clinical Electromyogram

Kevin C. Mcgill, Kenneth L. Cummins, Leslie J. Dorfman

Research output: Contribution to journalArticlepeer-review

169 Scopus citations

Abstract

We describe a new, automatic signal-processing method (ADEMG) for extracting motor-unit action potentials (MUAP’s) from the electromyographic interference pattern for clinical diagnostic purposes. The method employs digital filtering to select the spike components of the MUAP’s from the background activity, identifies the spikes by template matching, averages the MUAP waveforms from the raw signal using the identified spikes as triggers, and measures their amplitudes, durations, rise rates, numbers of phases, and firing rates. Efficient new algorithms are used to align and compare spikes and to eliminate interference from the MUAP averages. In a typical 10-s signal recorded from the biceps brachii muscle using a needle electrode during a 20 percent-maximal isometric contraction, the method identifies 8-15 simultaneously active MUAP’s and detects 30-70 percent of their occurrences. The analysis time is 90 s on a PDP-11/34A.

Original languageEnglish (US)
Pages (from-to)470-477
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
VolumeBME-32
Issue number7
DOIs
StatePublished - Jul 1985
Externally publishedYes

ASJC Scopus subject areas

  • Biomedical Engineering

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