A Novel Measure of Motor Unit Action Potential Variability in Nonstationary Surface Electromyograms
We introduce and validate a novel measure of motor unit action potential (MUAP) variability in surface electromyograms (EMG) that are recorded during dynamic muscle contractions. This measure is fully automatic, builds on the motor unit spike trains as estimated by previously introduced Convolution Kernel Compensation method and allows tracking of MUAP variability for each individual motor unit separately. Preliminary tests on synthetic surface EMG signals demonstrate its high accuracy and capability of identifying cyclostationary changes of MUAP shapes. This measure represents the first, but very important step towards motor unit identification in dynamic muscle contractions.
KeywordsMotor Unit Spike Train Motor Unit Action Potential Dynamic Contraction Surface Electromyogram
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