Miriam González-Izal

Electromyographic models to assess muscle fatigue

Muscle fatigue is a common experience in daily life. Many authors have defined it as the incapacity to maintain the required or expected force, and therefore, force, power and torque recordings have been used as direct measurements of muscle fatigue. In addition, the measurement of these variables combined with the measurement of surface electromyography (sEMG) recordings (which can be measured during all types of movements) during exercise may be useful to assess and understand muscle fatigue. Therefore, there is a need to develop muscle fatigue models that relate changes in sEMG variables with muscle fatigue. However, the main issue when using conventional sEMG variables to quantify fatigue is their poor association with direct measures of fatigue. Therefore, using different techniques, several authors have combined sets of sEMG parameters to assess muscle fatigue. The aim of this paper is to serve as a state-of-the-art summary of different sEMG models used to assess muscle fatigue. This paper provides an overview of linear and non-linear sEMG models for estimating muscle fatigue, their ability to assess power loss and their limitations due to neuromuscular changes after a training period.

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.

Energy metabolism during repeated sets of leg press exercise leading to failure or not.

This investigation examined the influence of the number of repetitions per set on power output and muscle metabolism during leg press exercise. Six trained men (age 34±6 yr) randomly performed either 5 sets of 10 repetitions (10REP), or 10 sets of 5 repetitions (5REP) of bilateral leg press exercise, with the same initial load and rest intervals between sets. Muscle biopsies (vastus lateralis) were taken before the first set, and after the first and the final sets. Compared with 5REP, 10REP resulted in a markedly greater decrease (P<0.05) of the power output, muscle PCr and ATP content, and markedly higher (P<0.05) levels of muscle lactate and IMP. Significant correlations (P<0.01) were observed between changes in muscle PCr and muscle lactate (R2 = 0.46), between changes in muscle PCr and IMP (R2 = 0.44) as well as between changes in power output and changes in muscle ATP (R2 = 0.59) and lactate (R2 = 0.64) levels. Reducing the number of repetitions per set by 50% causes a lower disruption to the energy balance in the muscle. The correlations suggest that the changes in PCr and muscle lactate mainly occur simultaneously during exercise, whereas IMP only accumulates when PCr levels are low. The decrease in ATP stores may contribute to fatigue.

Effects of Strength Training on Muscle Fatigue Mapping from Surface EMG and Blood Metabolites

PURPOSE this study examined the effects of heavy resistance training on the relationships between power loss and surface EMG (sEMG) indices and blood metabolite concentrations on dynamic exercise-induced fatigue with the same relative load as in pretraining. METHODS twelve trained subjects performed five sets consisting of 10 repetitions in the leg press, with 2 min of rest between sets before and after a strength training period. sEMG variables (the mean average voltage, the median spectral frequency, and the Dimitrov spectral index of muscle fatigue) from vastus medialis and lateralis muscles and metabolic responses (i.e., blood lactate, uric acid, and ammonia concentrations) were measured. RESULTS the peak power loss after the posttraining protocol was greater (61%) than the decline observed in the pretraining protocol (46%). Similar sEMG changes were found for both protocols, whereas higher metabolic demand was observed during the posttraining exercise. The linear models on the basis of the relations found between power loss and changes in sEMG variables were significantly different between pretraining and posttraining, whereas the linear models on the basis of the relations between power loss and changes in blood metabolite concentrations were similar. CONCLUSIONS linear models that use blood metabolites to map acute exercise-induced peak power changes were more accurate in detecting these changes before and after a short-term training period, whereas an attempt to track peak power loss using sEMG variables may fail after a strength training period.

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.

Muscle conduction velocity, strength, neural activity, and morphological changes after eccentric and concentric training

This study compared the effects of concentric and eccentric training on neuromuscular adaptations in young subjects. Twenty‐two men and women were assigned to one of two groups: concentric (CON, n = 11) and eccentric (ECC, n = 11) training. Training consisted of 6 weeks of isokinetic exercise, performed twice weekly, starting with two sets of eight repetitions, and progressing to five sets of 10 repetitions. Subjects were tested in strength variables [concentric, eccentric, and isometric peak torque (PT), and rate of force development (RFD)], muscle conduction velocity (CV), neuromuscular activity, vastus lateralis (VL) muscle thickness, and echo intensity as determined by ultrasonography. There were similar increases in the concentric and eccentric PTs in both the CON and ECC groups (P < 0.01), but only the ECC group showed an increase in isometric PT (P < 0.001). Similarly, both groups exhibited increased VL muscle thickness, CV, and RFD, and reduced VL echo intensity (P < 0.05). Significant correlations were observed among the relative changes in the neuromuscular outcomes and training variables (e.g., total work, average PT) (r = 0.68–0.75, P < 0.05). The results showed that both training types similarly improved dynamic PT, CV, RFD, and muscle thickness and quality during the early weeks of training.

MUSCLE CONDUCTION VELOCITY, SURFACE ELECTROMYOGRAPHY VARIABLES, AND ECHO INTENSITY DURING CONCENTRIC AND ECCENTRIC FATIGUE

Introduction: Concentric (CON) and eccentric (ECC) contractions may involve different mechanisms related to changes in sarcolemma status and the consequent alteration of action potential transmission along muscle fibers. Methods: Muscle conduction velocity (CV), surface electromyography signal (sEMG), muscle quality, and blood lactate concentrations were analyzed during CON and ECC actions. Results: Compared with ECC, the CON protocol resulted in greater muscle force losses, blood lactate concentrations, and changes in sEMG parameters. Similar reductions in CV were detected in both protocols. Higher echo intensity values were observed 2 days after ECC due to greater muscle damage. Conclusions: The effects of the muscle damage produced by ECC exercise on the transmission of action potentials along muscle fibers (measured as the CV) may be comparable with the effects of hydrogen accumulation produced by CON exercise (related to greater lactate concentrations), which causes greater force loss and change in other sEMG variables during CON than during ECC actions. Muscle Nerve 49:389–397, 2014

Anaerobic Energy Expenditure and Mechanical Efficiency during Exhaustive Leg Press Exercise

Information about anaerobic energy production and mechanical efficiency that occurs over time during short-lasting maximal exercise is scarce and controversial. Bilateral leg press is an interesting muscle contraction model to estimate anaerobic energy production and mechanical efficiency during maximal exercise because it largely differs from the models used until now. This study examined the changes in muscle metabolite concentration and power output production during the first and the second half of a set of 10 repetitions to failure (10RM) of bilateral leg press exercise. On two separate days, muscle biopsies were obtained from vastus lateralis prior and immediately after a set of 5 or a set of 10 repetitions. During the second set of 5 repetitions, mean power production decreased by 19% and the average ATP utilisation accounted for by phosphagen decreased from 54% to 19%, whereas ATP utilisation from anaerobic glycolysis increased from 46 to 81%. Changes in contraction time and power output were correlated to the changes in muscle Phosphocreatine (PCr; r = −0.76; P<0.01) and lactate (r = −0.91; P<0.01), respectively, and were accompanied by parallel decreases (P<0.01-0.05) in muscle energy charge (0.6%), muscle ATP/ADP (8%) and ATP/AMP (19%) ratios, as well as by increases in ADP content (7%). The estimated average rate of ATP utilisation from anaerobic sources during the final 5 repetitions fell to 83% whereas total anaerobic ATP production increased by 9% due to a 30% longer average duration of exercise (18.4±4.0 vs 14.2±2.1 s). These data indicate that during a set of 10RM of bilateral leg press exercise there is a decrease in power output which is associated with a decrease in the contribution of PCr and/or an increase in muscle lactate. The higher energy cost per repetition during the second 5 repetitions is suggestive of decreased mechanical efficiency.

Task Failure during Exercise to Exhaustion in Normoxia and Hypoxia Is Due to Reduced Muscle Activation Caused by Central Mechanisms While Muscle Metaboreflex Does Not Limit Performance

To determine whether task failure during incremental exercise to exhaustion (IE) is principally due to reduced neural drive and increased metaboreflex activation eleven men (22 ± 2 years) performed a 10 s control isokinetic sprint (IS; 80 rpm) after a short warm-up. This was immediately followed by an IE in normoxia (Nx, PIO2:143 mmHg) and hypoxia (Hyp, PIO2:73 mmHg) in random order, separated by a 120 min resting period. At exhaustion, the circulation of both legs was occluded instantaneously (300 mmHg) during 10 or 60 s to impede recovery and increase metaboreflex activation. This was immediately followed by an IS with open circulation. Electromyographic recordings were obtained from the vastus medialis and lateralis. Muscle biopsies and blood gases were obtained in separate experiments. During the last 10 s of the IE, pulmonary ventilation, VO2, power output and muscle activation were lower in hypoxia than in normoxia, while pedaling rate was similar. Compared to the control sprint, performance (IS-Wpeak) was reduced to a greater extent after the IE-Nx (11% lower P < 0.05) than IE-Hyp. The root mean square (EMGRMS) was reduced by 38 and 27% during IS performed after IE-Nx and IE-Hyp, respectively (Nx vs. Hyp: P < 0.05). Post-ischemia IS-EMGRMS values were higher than during the last 10 s of IE. Sprint exercise mean (IS-MPF) and median (IS-MdPF) power frequencies, and burst duration, were more reduced after IE-Nx than IE-Hyp (P < 0.05). Despite increased muscle lactate accumulation, acidification, and metaboreflex activation from 10 to 60 s of ischemia, IS-Wmean (+23%) and burst duration (+10%) increased, while IS-EMGRMS decreased (−24%, P < 0.05), with IS-MPF and IS-MdPF remaining unchanged. In conclusion, close to task failure, muscle activation is lower in hypoxia than in normoxia. Task failure is predominantly caused by central mechanisms, which recover to great extent within 1 min even when the legs remain ischemic. There is dissociation between the recovery of EMGRMS and performance. The reduction of surface electromyogram MPF, MdPF and burst duration due to fatigue is associated but not caused by muscle acidification and lactate accumulation. Despite metaboreflex stimulation, muscle activation and power output recovers partly in ischemia indicating that metaboreflex activation has a minor impact on sprint performance.