sEMG wavelet-based indices predicts muscle power loss during dynamic contractions.

The purpose of this study was to investigate the sensitivity of new surface electromyography (sEMG) indices based on the discrete wavelet transform to estimate acute exercise-induced changes on muscle power output during a dynamic fatiguing protocol. Fifteen trained subjects performed five sets consisting of 10 leg press, with 2 min rest between sets. sEMG was recorded from vastus medialis (VM) muscle. Several surface electromyographic parameters were computed. These were: mean rectified voltage (MRV), median spectral frequency (F(med)), Dimitrov spectral index of muscle fatigue (FI(nsm5)), as well as five other parameters obtained from the stationary wavelet transform (SWT) as ratios between different scales. The new wavelet indices showed better accuracy to map changes in muscle power output during the fatiguing protocol. Moreover, the new wavelet indices as a single parameter predictor accounted for 46.6% of the performance variance of changes in muscle power and the log-FI(nsm5) and MRV as a two-factor combination predictor accounted for 49.8%. On the other hand, the new wavelet indices proposed, showed the highest robustness in presence of additive white Gaussian noise for different signal to noise ratios (SNRs). The sEMG wavelet indices proposed may be a useful tool to map changes in muscle power output during dynamic high-loading fatiguing task.

Linear vs. non-linear mapping of peak power using surface EMG features during dynamic fatiguing contractions.

This study compares a non-linear (neural network) and a linear (linear regression) power mapping using a set of features of the surface electromyogram recorded from the vastus medialis and lateralis muscles. Fifteen healthy participants performed 5 sets of 10 repetitions leg press using the individual maximum load corresponding what they could perform 10 times (10RM) with 120s of rest between them. The following sEMG variables were computed from each extension contraction and used as inputs to both approaches: mean average value (MAV), median frequency (Fmed), the spectral parameter proposed by Dimitrov (FInsm5), average (over the observation interval) of the instantaneous mean frequency obtained from a Choi-Williams distribution (MFM), and wavelet indices ratio between moments at different scales (WIRM1551, WIRM1M51, WIRM1522, WIRE51, and WIRW51). The non-linear mapping (neural network) provided higher correlation coefficients and signal-to-noise ratios values (although not significantly different) between the actual and the estimated changes of power compared to linear mapping (linear regression) using the sEMG variables alone and a combination of WIRW51 and MFM (obtained by a stepwise multiple linear regression). In conclusion, non-linear mapping of force loss during dynamic knee extension exercise showed higher signal-to-noise ratio and correlation coefficients between the actual and estimated power output compared to linear mapping. However, since no significant differences were observed between linear and non-linear approaches, both were equally valid to estimate changes in peak power during fatiguing repetitive leg extension exercise.

EMG spectral indices and muscle power fatigue during dynamic contractions.

The purpose of this study was to examine acute exercise-induced changes on muscle power output and surface electromyography (sEMG) parameters (amplitude and spectral indices of muscle fatigue) during a dynamic fatiguing protocol. Fifteen trained subjects performed five sets consisting of 10 leg presses (10RM), with 2min rest between sets. Surface electromyography was recorded from vastus medialis (VM) and lateralis (VL) and biceps femoris (BF) muscles. A number of EMG-based parameters were compared for estimation accuracy and sensitivity to detect peripheral muscle fatigue. These were: Mean Average Voltage, median spectral frequency, Dimitrov spectral index of muscle fatigue (FI(nsm5)), as well as other parameters obtained from a time-frequency analysis (Choi-Williams distributions) such as mean and variance of the instantaneous frequency and frequency variance. The log FI(nsm5) as a single parameter predictor accounted for 37% of the performance variance of changes in muscle power and the log FI(nsm5) and MFM as a two factor combination predictor accounted for 44%. Peripheral impairments assessed by sEMG spectral index FI(nsm5) may be a relevant factor involved in the loss of power output after dynamic high-loading fatiguing task.

Neuromuscular fatigue after resistance training.

This study examined the effects of heavy resistance training on dynamic exercise-induced fatigue task (5 x 10RM leg-press) after two loading protocols with the same relative intensity (%) (5 x 10RM(Rel)) and the same absolute load (kg) (5 x 10RM(Abs)) as in pretraining in men (n=12). Maximal strength and muscle power, surface EMG changes [amplitude and spectral indices of muscle fatigue], and metabolic responses (i.e.blood lactate and ammonia concentrations) were measured before and after exercise. After training, when the relative intensity of the fatiguing dynamic protocol was kept the same, the magnitude of exercise-induced loss in maximal strength was greater than that observed before training. The peak power lost after 5 x 10RM(Rel) (58-62%, pre-post training) was greater than the corresponding exercise-induced decline observed in isometric strength (12-17%). Similar neural adjustments, but higher accumulated fatigue and metabolic demand were observed after 5 x 10RM(Rel). This study therefore supports the notion that similar changes are observable in the EMG signal pre- and post-training at fatigue when exercising with the same relative load. However, after training the muscle is relatively able to work more and accumulate more metabolites before task failure. This result may indicate that rate of fatigue development (i.e. power and MVC) was faster and more profound after training despite using the same relative intensity.