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The Influence of Force Level and Motor Unit Coherence on Nonlinear Surface EMG Features Examined Using Model Simulation
Date Issued
2019-07-27
Date Available
2020-02-13T13:09:00Z
Abstract
Nonlinear features extracted from surface EMG signals have been previously used to infer information on coherent or synchronous activity in the underlying motor unit discharges. However, it has not yet been assessed how these features are affected by the density of the surface EMG signal, and whether changes in the level of muscle activation can influence the effective detection of correlated motor unit firing. To examine this, a motoneuron pool model receiving a beta-band modulated cortical input was used to generate correlated motor unit firing trains. These firing trains were convolved with motor unit action potentials generated from an anatomically accurate electrophysiological model of the first dorsal interosseous muscle. The sample entropy (SampEn) and percentage determinism (%DET) of recurrence quantification analysis were calculated from the composite surface EMG signals, for signals comprised of both correlated and uncorrelated motor unit firing trains. The results show that although both SampEn and %DET are influenced by motor unit coherence, they are differentially affected by muscle activation and motor unit distribution. The results also suggest that sample entropy may provide a more accurate assessment of the underlying motor unit coherence than percentage determinism, as it is less sensitive to factors unrelated to motor unit synchrony.
Sponsorship
European Research Council
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2019 IEEE
Language
English
Status of Item
Peer reviewed
Part of
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Conference Details
The 41st International Engineering in Medicine and Biology Conference, Berlin, Germany, 23-27 July 2019
ISBN
978-1-5386-1311-5/19
This item is made available under a Creative Commons License
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McManus et al ForceLevelNonlinear_IEEE_preprint.pdf
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679.44 KB
Format
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