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J Appl Physiol 93: 1753-1763, 2002. First published August 9, 2002; doi:10.1152/japplphysiol.00314.2002
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Vol. 93, Issue 5, 1753-1763, November 2002

Nonlinear surface EMG analysis to detect changes of motor unit conduction velocity and synchronization

Dario Farina1, Luigi Fattorini2,3, Francesco Felici4, and Giancarlo Filligoi5,6

1 Centro di Bioingegneria, Dipartimento di Elettronica, Politecnico di Torino, Torino 10129; 2 Dipartimento Fisiologia Umana e Farmacologia, and 3 Scuola di Medicina dello Sport, Università di Roma, and 5 Facoltà di Ingegneria, Dipartimento INFOCOM and 6 Centro Interdipartimentale Sistemi Biomedici, Università degli Studi, La Sapienza, Roma 00185; and 4 Facoltà di Scienze Motorie, Istituto Universitario di Scienze Motorie, Roma 00194, Italy

Amplitude and frequency content of the surface electromyographic (EMG) signal reflect central and peripheral modifications of the neuromuscular system. Classic surface EMG spectral variables applied to assess muscle functions are the centroid and median power spectral frequencies. More recently, nonlinear tools have been introduced to analyze the surface EMG; among them, the recurrence quantification analysis (RQA) was shown to be particularly promising for the detection of muscle status changes. The purpose of this work was to analyze the effect of motor unit short-term synchronization and conduction velocity (CV) on EMG spectral variables and two variables extracted by RQA, the percentage of recurrence (%Rec) and determinism (%Det). The study was performed on the basis of a simulation model, which allowed changing the degree of synchronization and mean CV of a number of motor units, and of an experimental investigation of the surface EMG signal properties detected during high-force-level isometric fatiguing contractions of the biceps brachii muscle. Simulations and experimental results were largely in agreement and show that 1) spectral variables, %Rec, and %Det are influenced by CV and degree of synchronization; 2) spectral variables are highly correlated with %Det (R = -0.95 in the simulations and -0.78 and -0.75 for the initial values and normalized slopes, respectively, in the experimental signals), and thus the information they provide on muscle properties is basically the same; and 3) variations of %Det and %Rec in response to changes in muscle properties are significantly larger than the variations of spectral variables. This study validates RQA as a means for fatigue assessment with potential advantages (such as the higher sensitivity to changes of muscle status) with respect to the classic spectral analysis.

surface electromyography; motor unit short-term synchronization; surface electromyographic modeling; recurrence plot analysis


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