The aim of this study was to investigate the influence of high degrees of motor unit synchronization on surface EMG variables extracted by linear and non-linear analysis techniques. For this purpose, spectral and recurrent quantification analysis (RQA) were applied to both simulated and experimental EMG signals. Synthetic surface EMG signals were generated with a model of volume conductor comprising muscle, fat, and skin tissues. The synchronization was quantified by the percent of discharges of each motor unit synchronized with discharges of other motor units. The simulated signals presented degrees of synchronization in the range 0-80% (10% increments) and three mean values of motor unit conduction velocity distribution (3, 4 and 5 m/s). Experimental signals were collected from the first dorsal interosseous muscle of five patients with Parkinson disease during 10 s of rest and 10 s of isometric voluntary contraction at 50% of the maximal force. Mean power spectral frequency (MNF) and percent of determinism (%DET) of the surface EMG were computed from the simulated and experimental signals. In the simulated signals, %DET was linearly related to the level of synchronization in the entire range considered while MNF was sensitive to changes in synchronization in a smaller range (0-20%), outside which it levelled off. The experimental results indicated that %DET was significantly higher in the resting condition (with presence of tremor; mean ± S.E., 85.4 ± 0.8%) than during the voluntary contraction (which partly suppressed tremor; 60.0 ± 2.3%; P <0.01). On the contrary, MNF did not depend on the condition (114.3 ± 1.5 Hz and 118.0 ± 0.8 Hz for the resting and voluntary contraction, respectively), confirming the simulation results. Overall, these results indicated that linear and non-linear analyses of the surface EMG may have different sensitivities to the underlying physiological mechanisms in specific conditions, thus their joint use provides a more complete view of the muscle status than spectral analysis only.
- Motor unit synchronization
- Parkinson disease
- Recurrence quantification analysis
- Spectral analysis
ASJC Scopus subject areas