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.     

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.     

Quadriceps EMG frequency content following isometric lumbar extension exercise

A relationship exists between muscles of the lumbar spine and those of the lower extremity where the quadriceps become more inhibited after lumbar paraspinal. The purpose of this experiment was to compare surface electromyography (sEMG) total frequency content after lumbar paraspinal fatiguing exercise. Scope: 50 subjects performed fatiguing lumbar extension exercise indexed by downward shifts in median frequency calculated from lumbar paraspinal sEMG signal. Before and after each exercise set we recorded maximal, isometric knee extension torque and quadriceps central activation ratio (QI) using the superimposed burst technique while recording vastus lateralis sEMG. We calculated total frequency content of the sEMG signal (fEMG_TOTAL) as the area of the quadriceps sEMG frequency spectrum. Quadriceps fEMG_TOTAL decreased from baseline following the first and second exercise sets. There was no significant change in quadriceps sEMG median frequency among baseline and post-exercise measures. The change in fEMG_TOTAL was correlated with the change in QI following the first (r = ?0.41, P = 0.003) and second (r = ?0.32, P = 0.02) exercise sets. Conclusion: Quadriceps fEMG_TOTAL decreased following fatiguing lumbar extension exercise, in the absence of a significant change in quadriceps median frequency.     

等长收缩诱发肌肉疲劳及恢复过程中表面肌电信号特征变化规律

运用线性和非线性分析方法分析不同强度等长收缩诱发局部肌肉疲劳及恢复过程中表面肌电信号（surface electromyogram,sEMG）特征的变化规律,探讨影响sEMG信号变化的可能原因和机制.结果显示,在肱二头肌疲劳收缩过程中,sEMG的特征指标平均肌电值（average EMG,AEMG）、平均功率频率（mean power frequency,MPF）、Lempel-Ziv复杂度（Lempel-Ziv complexity,C（n））和确定性线段百分数（Determinism%,% DET）的变化具有良好的规律性.恢复期AEMG没有表现出规律性的变化,MPF、C（n）和?T在恢复期2秒即开始显著恢复,在前10秒恢复很快,随后恢复速度变慢.恢复初期sEMG信号特征的快速变化提示中枢控制因素可能发挥更大作用.     

静态负荷诱发肌肉疲劳后恢复期sEMG信号变化规律

目的:探讨肌肉疲劳过程中sEMG功率谱变化与H 的关系以及可能存在的其它影响因素.方法:利用肌肉进行疲劳收缩结束后,短时间内肌肉pH值尚无明显改变的特性,观察恢复期30 s内s EMG功率谱的变化规律.八名男性受试者,以肱二头肌为目标肌肉,负荷强度为60%MVC,静态持续负荷至疲劳点后,在恢复期以同样负荷分别观察2 s、4 s、6 s、8 s、10 s、20 s、30 s时的sEMG信号特征.结果:肱二头肌在以60%MVC静态疲劳负荷过程中MPF呈线性下降.在疲劳负荷后的恢复期,MPF恢复极其迅速,运动结束后仅2 s,MPF已恢复到整个下降范围的26.5%;至30 s,MPF已恢复到整个下降范围的87.7%.结论:由[H ]增加引起的肌纤维动作电位传导速度下降不是决定sEMG功率谱左移的唯一因素,提示sEMG功率谱左移可能与神经源性的中枢机制的作用有关.     

Biceps brachii myoelectric and oxygenation changes during static and sinusoidal isometric exercises

Surface myoelectric signal changes occurring during sustained isometric contractions have been extensively studied with quantitative surface electromyography (sEMG) and are described by means of some sEMG global variables in time and frequency domain (such as the median power spectral frequency). Recently, the possibility of studying local muscle O₂ saturation during exercise using non-invasive methods has been enhanced thanks to the use of near-infrared spectroscopy (NIRS). The purpose of this work was to combine NIRS and sEMG techniques to analyze the relationship between modifications of sEMG parameters and the underlying metabolic status of the exercising biceps brachii muscle. This relationship was tested under different isometric contraction modalities, namely static (ST) at 20, 40, 60 and 80%MVC and sinusoidal (SIN) at 40 ± 20 and 60 ± 20%MVC. Results clearly indicated the presence of an initial fast phase of muscle O₂ desaturation followed by a slow phase, regardless of the contraction modality. Moreover, the initial rate of muscle O₂ desaturation was related to the level of force output (R = 0.92), but it was independent on the contraction modality (p < 0.05). Similarly, changes in sEMG parameters were related to force level (Conduction Velocity-CV vs. Force: R = 0.87; sEMG Median Frequency-MDF vs. Force: R = 0.86). The high correlation found between CV-MDF and Tissue Oxygenation Index (TOI) slope (R = 0.73 and 0.72, respectively) suggests a strong relationship between NIRS and sEMG data. This study indicates that muscle O₂ demand during isometric contractions from low to high force levels is influenced by the type of active motor units and not from the type of isometric exercise modality.     

Does methocarbamol affect fatigue markers in the low-back electromyogram?

Many low-back patients undergo electromyography (EMG)-based evaluations of muscle performance but present to the clinic after being prescribed muscle relaxants. The question that needed to be addressed was, do centrally acting muscle relaxants (methocarbamol; Robaxin®) affect the EMG spectral indices of muscle fatigue that are often used to assess muscle performance. Participants performed an isometric spine extension protocol involving a 30 s fatigue exertion trial, then 1 min rest, and finally a 10 s long repeat exertion trial, at a 60% maximum voluntary contraction (MVC) level of exertion. Seven men were tested on two separate days (approximately 3–7 days apart), one day while medicated (six doses) with Robaxin and on another while not medicated. Specifically, the following parameters were studied in the bilateral multifidus (L5), lower erector spinae (L3) and upper erector spinae (T9): the slope of median power frequencies (MPFs) over the duration of the trial and the initial y-intercept of the MPF. The results generally suggest that methocarbamol (Robaxin) does not have any significant affect on the EMG median power frequency of the extensors during a fatiguing contraction followed by a repeat exertion, at least in normal people (one exception was observed—one side of multifidus at L5). However, given that this appears to be the first study of its kind, and that a relatively small number of subjects were used in this study, further investigation is needed to make a definitive conclusion about the effects of this drug on the several features of the electromyogram, over a broad spectrum of the clinical population performing a wider variety of tasks.     

Muscle fatigue and contraction intensity modulates the complexity of surface electromyography

Nonlinear dynamical techniques offer a powerful approach for the investigation of physiological time series. Multiscale entropy analyses have shown that pathological and aging systems are less complex than healthy systems and this finding has been attributed to degraded physiological control processes. A similar phenomenon may arise during fatiguing muscle contractions where surface electromyography signals undergo temporal and spectral changes that arise from the impaired regulation of muscle force production. Here we examine the affect of fatigue and contraction intensity on the short and long-term complexity of biceps brachii surface electromyography. To investigate, we used an isometric muscle fatigue protocol (parsed into three windows) and three contraction intensities (% of maximal elbow joint moment: 40%, 70% and 100%). We found that fatigue reduced the short-term complexity of biceps brachii activity during the last third of the fatiguing contraction. We also found that the complexity of surface electromyography is dependent on contraction intensity. Our results show that multiscale entropy is sensitive to muscle fatigue and contraction intensity and we argue it is imperative that both factors be considered when evaluating the complexity of surface electromyography signals. Our data contribute to a converging body of evidence showing that multiscale entropy can quantify subtle information content in physiological time series.     

Discrete wavelet transform analysis of surface electromyography for the fatigue assessment of neck and shoulder muscles

Assessment of neuromuscular fatigue is essential for early detection and prevention of risks associated with work-related musculoskeletal disorders. In recent years, discrete wavelet transform (DWT) of surface electromyography (SEMG) has been used to evaluate muscle fatigue, especially during dynamic contractions when the SEMG signal is non-stationary. However, its application to the assessment of work-related neck and shoulder muscle fatigue is not well established. Therefore, the purpose of this study was to establish DWT analysis as a suitable method to conduct quantitative assessment of neck and shoulder muscle fatigue under dynamic repetitive conditions. Ten human participants performed 40 min of fatiguing repetitive arm and neck exertions while SEMG data from the upper trapezius and sternocleidomastoid muscles were recorded. The ten of the most commonly used wavelet functions were used to conduct the DWT analysis. Spectral changes estimated using power of wavelet coefficients in the 12–23 Hz frequency band showed the highest sensitivity to fatigue induced by the dynamic repetitive exertions. Although most of the wavelet functions tested in this study reasonably demonstrated the expected power trend with fatigue development and recovery, the overall performance of the “Rbio3.1” wavelet in terms of power estimation and statistical significance was better than the remaining nine wavelets.     

Muscle endurance time estimation during isometric training using electromyogram and supervised learning

Constant-force isometric muscle training is useful for increasing the maximal strength , rehabilitation and work-fatigue assessment. Earlier studies have shown that muscle fatigue characteristics can be used for evaluating muscle endurance limit. Study Objective: To predict muscle endurance time during isometric task using frequency spectrum characteristics of surface electromyography signals along with analysis of frequency spectrum shape and scale during fatigue accumulation. Method: Thirteen subjects performed isometric lateral raise at 60% MVC of deltoid (lateral) till endurance limit. Time windowed sEMG frequency spectrum was modelled using 2-parameter distributions namely Gamma and Weibull for spectrum analysis and endurance prediction. Results: Gamma distribution provided better spectrum fitting (P < 0.001) than Weibull distribution. Spectrum Distribution demonstrated no change in shape but shifted towards lower frequency with increase of magnitude at characteristic mode frequency. Support Vector Regression based algorithm was developed for endurance time estimation using features derived from fitted frequency spectrum. Time taken till endurance limit for acquired dataset 38.53 ± 17.33 s (Mean ± Standard Deviation) was predicted with error of 0.029 ± 4.19 s . R-square: 0.956, training and test sets RMSE was calculated as 3.96 and 4.29 s respectively. The application of the algorithm suggested that model required 70% of sEMG signal from maximum time of endurance for high prediction accuracy. Conclusion: Endurance Limit prediction algorithm was developed for quantification of endurance time for optimizing isometric training and rehabilitation. Our method could help personalize and change conventional training method of same weight and duration for all subjects with optimized training parameters, based upon individual sEMG activity.     

Catapult effect in pole vaulting: Is muscle coordination determinant?

This study focused on the phase between the time of straightened pole and the maximum height (HP) of vaulter and aimed at determining the catapult effect in pole vaulting on HP. Seven experienced vaulters performed 5-10 vaults recorded by two video cameras, while the surface electromyography (sEMG) activity of 10 upper limbs muscles was recorded. HP was compared with an estimated maximum height (HP_est) allowing the computation of a push-off index. Muscle synergies were extracted from the sEMG activity profiles using a non-negative matrix factorization algorithm. No significant difference (p > 0.47) was found between HP_est (4.64 ± 0.21 m) and HP (4.69 ± 0.23 m). Despite a high inter-individual variability in sEMG profiles, two muscle synergies were extracted for all the subjects which accounted for 96.1 ± 2.9% of the total variance. While, the synergy activation coefficients were very similar across subjects, a higher variability was found in the muscle synergy vectors. Consequently, whatever the push-off index among the pole vaulters, the athletes used different muscle groupings (i.e., muscle synergy vectors) which were activated in a similar fashion (i.e., synergy activation coefficients). Overall, these results suggested that muscle coordination adopted between the time of straightened pole and the maximum height does not have a major influence on HP.     

The use of correlation integrals in the study of localized muscle fatigue of elbow flexors during maximal efforts.

Innovative applications of non-linear time series analysis have recently been used to investigate physiological phenomena. In this study, we investigated the feasibility of using the correlation integral to monitor the localized muscle fatigue process in the biceps brachii during sustained maximal efforts. The subjects performed isometric maximum contractions until failure in elbow flexion (90 degrees from neutral). The median and the 70th percentile frequency of the Surface electromyography (SEMG) power spectrum, the integrated SEMG, and the Correlation Integral (CI) were evaluated during the trials. The linear correlation between these variables and the elbow torque production was used to quantify the ability of a parameter to follow the fatiguing process. The CI had the highest linear correlation with torque (0.77 (0.12SD)), while the spectral indices correlations with torque were much lower. The decreasing trend of the torque production was followed by the spectral indices for only the beginning part of the contraction, while the CI increased sharply after the torque production fell to about 0.60 of the MVC. This suggests that the CI is sensitive to different changes of the SEMG signal during fatigue than the spectral variables.     

Muscle fatigue and calibration of EMG measurements

Amplitude electromyography (EMG) is often used as an estimator of muscular load. Such measurements can, however, be biased by other factors, for example muscular fatigue. The aim of this study was to examine the influence of fatigue on amplitude parameters of the EMG. The test subjects raised the arm to 90⁹ of abduction in the plane of the scapula. The hand was loaded with 0, 1 and 2 kg during 5, 3 and 2 min respectively. EMG was recorded from the trapezius muscle, and spectral and amplitude parameters were calculated. There was a significant rise of the EMG amplitude as a sign of fatigue at all load levels: 7% min⁻¹ at 0 kg, 15% min⁻¹ at 1 kg, and 19% min⁻¹ at 2 kg. At 0 kg hand load there was no change of the spectral parameters but at higher load levels there was a significant decline of mean power frequency: 3% min⁻¹ at 1 kg and 11% at 2 kg. The amplitude rise due to muscle fatigue may seriously jeopardize calibration measurements unless the duration of the load is kept limited.     

Changes in electromyogram power spectra of facial and jaw-elevator muscles during fatigue

Endurance and changes in electromyogram (EMG) power spectra were investigated during a fatiguing static contraction at 50% of the maximum EMG amplitude in two jaw-elevator muscles (masseter and temporalis) and five facial muscles (frontalis, corrugator supercilii, zygomaticus major, orbicularis oris, and buccinator). Relatively high endurance was found in orbicularis oris, frontalis, and corrugator supercilii muscles; intermediate endurance was found in zygomaticus major, buccinator, and temporalis muscles; and low endurance was found in the masseter muscle. The last muscle showed a relatively fast linear decrease of the median frequency of the power spectrum. The other muscles showed a much slower, exponential decrease. The median frequency appeared to reflect reliably the changes in the shape of the power spectra during fatigue. Large differences between the shape of power spectra of different muscles in the unfatigued state were found. These, however, were unrelated to endurance and degree of spectral shift during fatigue.     

Assessment of muscle hardness changes induced by a submaximal fatiguing isometric contraction

Transient elastography consists of measuring the transverse local shear elastic modulus defined as local muscle hardness (LMH). It has previously been shown that LMH is correlated to muscle activity level during non-fatiguing contractions. The aim of this study was to describe how LMH and muscle activity level change during a submaximal fatiguing constant-torque protocol. Changes in gastrocnemius medialis LMH and in surface electromyographic activities (sEMG) of plantar flexors induced by a submaximal isometric plantar flexion (40% of the maximal isometric torque) until exhaustion were quantified. During the contraction, sEMG of each muscle increased (P<0.001) whereas LMH remained constant (P>0.05). Active LMH assessed during the contraction did not parallel muscle activity level changes during this type of submaximal fatigue protocol. Interestingly, LMH at rest assessed in passive conditions was higher prior to the fatiguing effort (P<0.05), rather than that assessed immediately after. Muscle and tendon viscous behaviors could imply a creep phenomenon during a prolonged isometric contraction, and our results in LMH at rest could indicate that this phenomenon induces changes in muscle intrinsic mechanical properties. Further studies are needed to examine whether it could have an influence on muscle activity levels during the contraction.     

Contralateral muscle activity and fatigue in the human first dorsal interosseous muscle.

During effortful unilateral contractions, muscle activation is not limited to the target muscles but activity is also observed in contralateral muscles. The amount of this associated activity is depressed in a fatigued muscle, even after correction for fatigue-related changes in maximal force. In the present experiments, we aimed to compare fatigue-related changes in associated activity vs. parameters that are used as markers for changes in central nervous system (CNS) excitability. Subjects performed brief maximal voluntary contractions (MVCs) with the index finger in abduction direction before and after fatiguing protocols. We followed changes in MVCs, associated activity, motor-evoked potentials (MEP; transcranial magnetic stimulation), maximal compound muscle potentials (M waves), and superimposed twitches (double pulse) for 20 min after the fatiguing protocols. During the fatiguing protocols, associated activity increased in contralateral muscles, whereas afterwards the associated force was reduced in the fatigued muscle. This force reduction was significantly larger than the decline in MVC. However, associated activity (force and electromyography) remained depressed for only 5-10 min, whereas the MVCs stayed depressed for over 20 min. These decreases were accompanied by a reduction in MEP, MVC electromyography activity, and voluntary activation in the fatigued muscle. According to these latter markers, the decrease in CNS motor excitability lasted much longer than the depression in associated activity. Differential effects of fatigue on (associated) submaximal vs. maximal contractions might contribute to these differences in postfatigue behavior. However, we cannot exclude differences in processes that are specific to either voluntary or to associated contractions.     

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 120 s 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.     

Detection of surface electromyography recording time interval without muscle fatigue effect for biceps brachii muscle during maximum voluntary contraction

The effects of fatigue on maximum voluntary contraction (MVC) parameters were examined by using force and surface electromyography (sEMG) signals of the biceps brachii muscles (BBM) of 12 subjects. The purpose of the study was to find the sEMG time interval of the MVC recordings which is not affected by the muscle fatigue. At least 10 s of force and sEMG signals of BBM were recorded simultaneously during MVC. The subjects reached the maximum force level within 2 s by slightly increasing the force, and then contracted the BBM maximally. The time index of each sEMG and force signal were labeled with respect to the time index of the maximum force (i.e. after the time normalization, each sEMG or force signal’s 0 s time index corresponds to maximum force point). Then, the first 8 s of sEMG and force signals were divided into 0.5 s intervals. Mean force, median frequency (MF) and integrated EMG (iEMG) values were calculated for each interval. Amplitude normalization was performed by dividing the force signals to their mean values of 0 s time intervals (i.e. ?0.25 to 0.25 s). A similar amplitude normalization procedure was repeated for the iEMG and MF signals. Statistical analysis (Friedman test with Dunn’s post hoc test) was performed on the time and amplitude normalized signals (MF, iEMG). Although the ANOVA results did not give statistically significant information about the onset of the muscle fatigue, linear regression (mean force vs. time) showed a decreasing slope (Pearson-r = 0.9462, p < 0.0001) starting from the 0 s time interval. Thus, it might be assumed that the muscle fatigue starts after the 0 s time interval as the muscles cannot attain their peak force levels. This implies that the most reliable interval for MVC calculation which is not affected by the muscle fatigue is from the onset of the EMG activity to the peak force time. Mean, SD, and range of this interval (excluding 2 s gradual increase time) for 12 subjects were 2353, 1258 ms and 536–4186 ms, respectively. Exceeding this interval introduces estimation errors in the maximum amplitude calculations of MVC–sEMG studies for BBM. It was shown that, simultaneous recording of force and sEMG signals was required to calculate the maximum amplitude of the MVC–sEMG more accurately.     

Physiological characteristics of motor units in the brachioradialis muscle across fatiguing low-level isometric contractions.

The purpose of this study was to determine (i) if decomposition-based quantitative electromyography (DQEMG) could detect changes in motor unit potential (MUP) morphology and motor unit (MU) firing pattern statistics associated with muscle fatigue, (ii) if any detected changes are correlated with surface electromyographic (SEMG) signs of fatigue, and (iii) if significant fatigue-dependent changes are repeatable within individuals. Mean MU firing rates and the morphology of MUPs detected using needle and surface electrodes during constant-torque isometric contractions held until exhaustion were investigated in the brachioradialis (BR) muscle in 10 healthy volunteers (mean age=28.6 yr, SD+/-3.9). Time dependant changes were investigated using an analysis of variance with normalized time as a main effect. Partial correlation coefficients were computed using a repeated measures analysis of covariance to determine if changes in MU firing rates, needle-detected MUPs and surface-detected MUPs (SMUPs) were related to changes in SEMG signal amplitude and frequency parameters. Intraclass correlation coefficients (ICCs) were used to determine the within-subject repeatability of changes in MU firing rates, and MUP and SMUP parameters. Significant decreases in mean MU firing rates were found along with significant increases in various duration and area related parameters in both MUPs and SMUPs across the fatiguing contraction. The SEMG signal demonstrated the expected changes with fatigue: an increase in amplitude and a decrease in frequency content. SEMG amplitude was significantly positively correlated with SMUP peak-to-peak voltage (r=0.85, p<0.05), and SMUP area (r=0.86, p<0.05). Mean power frequency was significantly negatively correlated with SMUP negative peak duration (r=-0.74, p<0.05). The significant time-dependent changes were reliably observed (ICCs were 0.94 for MUP peak to peak amplitude, 0.97 for MUP area and 0.95 for MUP area to amplitude ratio, 0.95 for SMUP peak-to-peak voltage, 0.83 for SMUP area, 0.99 for SMUP negative peak amplitude and 0.88 for SMUP negative peak area). The decreases in mean MU firing rates measured along with the increases in amplitude, duration and area parameters of MUPs and SMUPs and their partial correlation with SEMG amplitude during submaximal fatiguing contractions of the BR, suggest that recruitment is a main cause of increased SEMG amplitude parameters with fatigue. We conclude that DQEMG can be effectively and reliably used to detect changes in physiological characteristics of MUs that accompany fatigue.