Effect of low frequency fatigue on human muscle strength and fatigability during subsequent stimulated activity

Fatiguing contractions of the adductor pollicis muscle were produced by intermittent supramaximal stimulation of the ulnar nerve in a set frequency pattern, in six normal subjects. At the end of an initial fatiguing contraction series, low frequency fatigue (LFF) had been induced and persisted at 15 min of recovery. Stimulated fatiguing activity was then repeated in an identical fashion to the initial series. At high frequencies, declines in force were similar for both series. At low frequencies, declines in force were greater during the second series despite similar changes in compound muscle action potential amplitude. This confirmation that LFF persists during subsequent stimulated activity, and reduces low but not high frequency fatigue resistance, suggests that the impaired endurance of fatigued muscle during voluntary activity primarily results from peripheral changes at low frequency. These findings also have implications for therapeutic electrical stimulation of muscle.     

Microcirculation in the upper trapezius muscle during sustained shoulder load in healthy women an endurance study using percutaneous laser-Doppler flowmetry and surface electromyography

Microcirculation in the upper portion of the trapezius muscle was measured percutaneously in a group of 16 healthy women of different ages by continuous laser-Doppler flowmetry (LDF) in relation to electromyography (EMG) during an endurance test. During the measurements the subject kept her arms straight and elevated at 45° in the scapular plane and held a 1-kg load in each hand as long as possible. This was followed by rest with the arms hanging and carrying no load. The 10-min recording period comprised 1-min initial rest followed by the endurance test and then recovery. Signal processing was done by computer on line. The LDF and root-mean-square (rms) EMG signals were normalized. Spectrum analyses of EMG mean power frequency (MPF) were performed. The amount of load produced was on average 2,267 (SD 939) N · m · s, i.e. shoulder torque × time expressed as Newton meter seconds, and the endurance time was 4.3 (SD 1.20) min. The rms-EMG as well as the LDF increased significantly during endurance, both when related to endurance time and to amount of load. The MPF showed no significant changes. The mean total increase in muscle blood flow was 175% of that recorded in the initial rest period. The average increase per each 10 s of contraction was 2.9%. Maximum was reached during the 1st min of recovery followed by a fall to the base level that was reached within 77 s on average. The amount of load produced and the blood flow increase was smaller than that found in a separate study of men, indicating a lower functional capacity. This may be of importance for the development of neck-shoulder disability in women.     

Surface EMG signal processing during isometric contractions

This paper provides an overview of techniques suitable for the estimation, interpretation and understanding of time variations that affect the surface electromyographic (EMG) signal during sustained voluntary or electrically elicited contractions. These variations concern amplitude variables, spectral variables and muscle fiber conduction velocity, are interdependent and are referred to as the ‘fatigue plot'. The fatigue plot provides information suitable for the classification of muscle behavior. In addition, the information obtainable by means of linear electrode arrays is discussed, and applications of mathematical models for the interpretation of array signals are presented. The model approach provides additional ways for the classification of muscle behavior.     

Factors affecting the turns analysis of the interference EMG signal

This study was performed to evaluate the relative significance of changes typical for muscle fatigue on quantitative parameters obtained from turns analysis of simulated intramuscular and surface interference electromyographic (EMG) signals. Effects of reduction of firing rate of motor units (MUs) and changes of intracellular action potential (IAP) profile along active fibers were analyzed. A new analytic function was proposed to simulate changes in IAP shape at different stages of muscle fatigue. In intramuscular EMG, both the decrease in firing rate of MUs and the changes in IAP profile led to reduction in the number of turns per second (NTs) and mean turn amplitude (MTA). The development of fatigue and especially the changes in IAP profile could explain why NTs increased up to only about 50% of maximal voluntary contraction, and remained unchanged above that level of efforts or even decreased. These effects should be especially pronounced in patients with myopathy whose IAP and muscle fatigability are expected to be abnormal. In surface EMG, the MTA increased considerably with fatigue; the sensitivity of NTs to reduction in firing rate (or number of discharges) was low. Thus, the benefits of the turns analysis of surface EMG signals should be lower not only in diagnosis of myopathy but also neuropathy.     

EMG median power frequency in an exhausting exercise

EMG median power frequency of the calf muscles was investigated during an exhausting treadmill exercise. This exercise was an uphill run, the average endurance time was 1.5 min. Median power frequency of the calf muscles declined by more than 10% during this exercise. In addition EMG median power frequency of isometric contractions of the same muscles was measured before and in one minute intervals for 10 min after this run. Immediately after the run isometric median power frequency had declined by less than 5% for the soleus muscle, more than 10% for the gastrocnemius medialis and gastrocnemius lateralis muscles. In the 10 min following exercise the isometric median power frequency increased to pre-execise levels. Maybe the median power frequency shift to lower frequencies during dynamic exercise can be interpreted as a sign of local muscle fatigue.     

Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment

Transhumeral amputation has a significant effect on a person’s independence and quality of life. Myoelectric prostheses have the potential to restore upper limb function, however their use is currently limited due to lack of intuitive and natural control of multiple degrees of freedom. The goal of this study was to evaluate a novel transhumeral prosthesis controller that uses a combination of kinematic and electromyographic (EMG) signals recorded from the person’s proximal humerus. Specifically, we trained a time-delayed artificial neural network to predict elbow flexion/extension and forearm pronation/supination from six proximal EMG signals, and humeral angular velocity and linear acceleration. We evaluated this scheme with ten able-bodied subjects offline, as well as in a target-reaching task presented in an immersive virtual reality environment. The offline training had a target of 4° for flexion/extension and 8° for pronation/supination, which it easily exceeded (2.7° and 5.5° respectively). During online testing, all subjects completed the target-reaching task with path efficiency of 78% and minimal overshoot (1.5%). Thus, combining kinematic and muscle activity signals from the proximal humerus can provide adequate prosthesis control, and testing in a virtual reality environment can provide meaningful data on controller performance.     

Cross-correlation of bilateral differences in fatigue during sustained maximal voluntary contraction

Maximal isometric force and electromyograph (EMG) activity of biceps brachii muscle during bilateral sustained elbow flexion were followed in 25 right-handed oarsmen. The percentage decline in force was greater for the left than for the right arm. Also, the mean power frequency (MPF) and the root mean square (rms) value of the EMG amplitude decreased more for the left than for the right arm. It was hypothesized that a common drive would indicate that the two forces curves would be highly correlated during the nonfatigued period, but the level of cross-correlation would decline during muscle fatigue. For the first 4 s of the contraction, the cross-correlation between the right and left force was high (r = 0.99), but thereafter it declined rapidly to a constant level. The decline of the cross-correlation was accompanied by a similar decrease in the correlation between the right and left EMG activations (MPF and rms). Thus, the decline in the cross-correlation level of force accompanied by a similar decrease in the correlation level of EMG would suggest a fatigue-induced neural derangement of the common drive.     

Modelling evolutionary cell behaviour using neural networks: Application to tumour growth

In this paper, we present a modelling framework for cellular evolution that is based on the notion that a cell’s behaviour is driven by interactions with other cells and its immediate environment. We equip each cell with a phenotype that determines its behaviour and implement a decision mechanism to allow evolution of this phenotype. This decision mechanism is modelled using feed-forward neural networks, which have been suggested as suitable models of cell signalling pathways. The environmental variables are presented as inputs to the network and result in a response that corresponds to the phenotype of the cell. The response of the network is determined by the network parameters, which are subject to mutations when the cells divide. This approach is versatile as there are no restrictions on what the input or output nodes represent, they can be chosen to represent any environmental variables and behaviours that are of importance to the cell population under consideration. This framework was implemented in an individual-based model of solid tumour growth in order to investigate the impact of the tissue oxygen concentration on the growth and evolutionary dynamics of the tumour. Our results show that the oxygen concentration affects the tumour at the morphological level, but more importantly has a direct impact on the evolutionary dynamics. When the supply of oxygen is limited we observe a faster divergence away from the initial genotype, a higher population diversity and faster evolution towards aggressive phenotypes. The implementation of this framework suggests that this approach is well suited for modelling systems where evolution plays an important role and where a changing environment exerts selection pressure on the evolving population.     

Reliability of muscle fibre conduction velocity distribution estimation from surface EMG

Most of the neuromuscular diseases induce changes in muscle fibre characteristics. For example, Duchenne dystrophy is characterized by a specific loss of fast fibres, and an increase in small diameter fibres. These morphological changes may lead to large modifications in the distribution of fibre diameters, possibly producing bimodal distributions. It has already been shown that it is possible to reveal these morphological modifications through the distribution of muscle fibre conduction velocity (MFCV) as estimated from needle electromyography (EMG) recordings. In this paper, we investigate whether such changes can be extracted from surface EMG signals.

Simulation allows generation of surface EMG signals in which features are well described especially at a morphological level. Therefore, we generated a database of simulated signals both in voluntary and electrically elicited contraction conditions using a bimodal distribution of muscle fibre diameters. MFCV distributions were computed using two short-term methods based on cross-correlation and peak-to-peak techniques for voluntary contraction signals, and using a deconvolution method in time domain for electrically elicited signals. MFCV distributions were compared with true ones, as generated from modelling.

This work reveals that estimating MFCV distribution through these methods does not appear yet as precise and robust enough to accurately characterize changes in redistribution of various muscle fibre diameters. However, it opens to new experimental protocols that can be explored in order to improve the robustness of MFCV distribution estimation for the follow-up of patients suffering from neuromuscular disorders.     

Estimation of muscle fiber conduction velocity from two-dimensional surface EMG recordings in dynamic tasks

The aims of this study are (1) to demonstrate that multi-channel surface electromyographic (EMG) signals can be detected with negligible artifacts during fast dynamic movements with an adhesive two-dimensional (2D) grid of 64 electrodes and (2) to propose a new method for the estimation of muscle fiber conduction velocity from short epochs of 2D EMG recordings during dynamic tasks. Surface EMG signals were collected from the biceps brachii muscle of four subjects with a grid of 13 × 5 electrodes during horizontal elbow flexion/extension movements (range 120–170°) at the maximum speed, repeated cyclically for 2 min. Action potentials propagating between the innervation zone and tendon regions could be detected during the dynamic task. A maximum likelihood method for conduction velocity estimation from the 2D grid using short time intervals was developed and applied to the experimental signals. The accuracy of conduction velocity estimation, assessed from the standard deviation of the residual of the regression line with respect to time, decreased from (range) 0.20–0.33 m/s using one column to 0.02–0.15 m/s when combining five columns of the electrode grid. This novel method for estimation of muscle fiber conduction velocity from 2D EMG recordings provides an estimate which is global in space and local in time, thus representative of the entire muscle yet able to track fast changes over the execution of a task, as is required for assessing muscle properties during fast movements.     

A method for assessing the equivalence of repeated measures of muscle fatigue rates estimated from EMG power spectrum analysis

This report addresses some of the statistical problems that are encountered when the test/retest recording reliability of fatigue-related parameters of the EMG power spectrum is evaluated. It can be shown that some classical methods for reliability assessment such as correlational procedures are unsuitable for this purpose. Because the EMG power spectrum fatigue parameter depends on metabolic changes in muscle tissue, it is suggested that methods similar to those used in the evaluation of bioequivalence studies may be more appropriate in the assessment of such test/retest results.     

Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis

A method for simultaneous, nondestructive analysis of aminopyrine and phenacetin in compound aminopyrine phenacetin tablets with different concentrations has been developed by principal component artificial neural networks (PC-ANNs) on near-infrared (NIR) spectroscopy. In PC-ANN models, the spectral data were initially analyzed by principal component analysis. Then the scores of the principal components were chosen as input nodes for the input layer instead of the spectral data. The artificial neural network models using the spectral data as input nodes were also established and compared with the PC-ANN models. Four different preprocessing methods (first-derivative, second-derivative, standard normal variate (SNV), and multiplicative scatter correction) were applied to three sets of NIR spectra of compound aminopyrine phenacetin tablets. The PC-ANNs approach with SNV preprocessing spectra was found to provide the best results. The degree of approximation was performed as the selective criterion of the optimum network parameters.     

Fatigue response of rat medial longissimus muscles induced with electrical stimulation at various work/rest ratios

Muscle fatigue may be a precursor to workplace musculoskeletal disorders, with the low back resulting in the most frequently injured body part. Work/rest ratios have an effect on fatigue due to the amount of rest allowance provided following muscle contraction. This study explored various work/rest ratios by electrically stimulating rat medial longissimus muscles. A 3 V stimulus with 0.2 ms pulse duration was applied at a frequency of 30 Hz. There were four stimulation groups consisting of the following duty cycles (DC) and cycle times (CT): DC25%:CT20s, DC25%:CT280s, DC75%:CT20s, and DC75%:CT180s. Muscle fatigue was measured as a decrease in M-wave amplitude and area, and an increase in M-wave duration. The results indicated that fatigue occurred immediately in each of the groups. The higher duty cycle and shorter cycle time group resulted in significantly greater fatigue than the lower duty cycle and longer cycle time group, as measured by increased M-wave amplitude and area. A longer M-wave duration was observed in the high duty cycle long cycle time group. This suggests that the combination of low duty cycle and long cycle times leads to less fatigue. In high duty cycle scenarios, short cycle times result in less fatigue.     

Validation of a portable EMG device to assess muscle activity during free-living situations

Portable amplifiers that record electromyograms (EMGs) for longer than four hours are commonly priced over $20,000 USD. This cost, and the technical challenges associated with recording EMGs during free-living situations, typically restrict EMG use to laboratory settings. A low-cost system (μEMG; OT Bioelecttronica, 100€), using specialized concentric bipolar electrodes, has been developed specifically for free-living situations. The purpose of this study was to validate the μEMG system by comparing EMGs from μEMG with a laboratory-based alternative (Telemyo 900; Noraxon USA, Inc.). Surface EMGs from biceps brachii (BB) and tibialis anterior (TA) of ten subjects were recorded simultaneously with both systems as subjects performed maximal voluntary contractions (MVCs), submaximal contractions at 25%, 50%, and 75% MVC, seven simulated activities of daily living (ADLs), and >60 min of simulated free-living inside the laboratory. In general, EMG parameters (e.g., average full-wave rectified EMG amplitude) derived from both systems were not significantly different for all outcome variables, except there were small differences across systems in baseline noise and absolute EMG amplitudes during MVCs. These results suggest that μEMG is a valid approach to the long-term recording of EMG.     

Comparative modeling of the conformational stability of chymotrypsin inhibitor 2 protein mutants using amino acid sequence autocorrelation (AASA) and amino acid 3D autocorrelation (AA3DA) vectors and ensembles of Bayesian-regularized genetic neural networks

Predicting protein stability changes upon point mutation is important for understanding protein structure and designing new proteins. Autocorrelation vector formalism was extended to amino acid sequences and 3D conformations for encoding protein structural information with modeling purpose. Protein autocorrelation vectors were weighted by 48 amino acid/residue properties selected from the AAindex database. Ensembles of Bayesian-regularized genetic neural networks (BRGNNs) trained with amino acid sequence autocorrelation (AASA) vectors and amino acid 3D autocorrelation (AA3DA) vectors yielded predictive models of the change of unfolding Gibbs free energy change (ΔΔG) of chymotrypsin Inhibitor 2 protein mutants. The ensemble predictor described about 58 and 72% of the data variances in test sets for AASA and AA3DA models, respectively. Optimum sequence and 3D-based ensembles exhibit high effects on relevant structural (volume, solvent-accessible surface area), physico-chemical (hydrophilicity/hydrophobicity-related) and thermodynamic (hydration parameters) properties.     

Progressive resistance training in neuromuscular patients. Effects on force and surface EMG

In a randomized clinical trial the efficacy of strength training was studied in patients with myotonic dystrophy (n=33) and in patients with Charcot-Marie-Tooth disease (n=29). Measurements were performed at the start and after 8, 16 and 24 weeks of progressive resistance training. Surface electromyography (SEMG) of proximal leg muscles was recorded during isometric knee extension at maximum voluntary contraction (MVC) and at 20, 40, 60 and 80% of MVC. Changes in MVC, maximum electrical activity and torque–EMG ratios (TER) were calculated. Fatigue was studied by determining the changes in endurance and in the decline of the median frequency (F_med) of the SEMG during a sustained contraction at 80% MVC. These parameters showed no significant changes after the training in either of the diagnostic groups. Only the Charcot-Marie-Tooth training group showed a gradual significant increase in mean MVC over the whole training period (21%). After 24 weeks, the increase in mean RMS was similar (25%), but this was mainly due to a sharp rise during the first 8 weeks of training (20%). The findings indicate that the initial strength increase was due to a neural factor, while the subsequent increase was mainly due to muscle hypertrophy.     

Comparison of surface electromyography and myotonometric measurements during voluntary isometric contractions

Objectives: Muscle stiffness increases during muscle contraction. The purpose of this study was to determine the strength of the correlation between myotonometric measurements of muscle stiffness and surface electromyography (sEMG) measurements during various levels of voluntary isometric contractions of the biceps brachii muscle. Subjects: Eight subjects (four female; four male), with mean age of 30.6±8.23 years, volunteered to participate in this study. Methods: Myotonometer and sEMG measurements were taken simultaneously from the right biceps brachii muscle. Data were obtained: (1) at rest, (2) while the subject held a 15 lb (6.8 kg) weight isometrically and, (3) during a maximal voluntary isometric contraction. Myotonometer force–displacement curves (amount of tissue displacement to a given unit of force applied perpendicular to the muscle) were compared with sEMG measurements using Pearson’s product–moment correlation coefficients. Results: Myotonometer and sEMG measurement correlations ranged from −0.70 to −0.90. The strongest correlations to sEMG were from Myotonometer force measurements between 1.00 and 2.00 kg. Conclusions: Myotonometer and sEMG measurements were highly correlated. Tissue stiffness, as measured by the Myotonometer, appears capable of assessing changes in muscle activation levels.     

Improving surface EMG burst detection in infrahyoid muscles during swallowing using digital filters and discrete wavelet analysis

The visual inspection is a widely used method for evaluating the surface electromyographic signal (sEMG) during deglutition, a process highly dependent of the examiners expertise. It is desirable to have a less subjective and automated technique to improve the onset detection in swallowing related muscles, which have a low signal-to-noise ratio. In this work, we acquired sEMG measured in infrahyoid muscles with high baseline noise of ten healthy adults during water swallowing tasks. Two methods were applied to find the combination of cutoff frequencies that achieve the most accurate onset detection: discrete wavelet decomposition based method and fixed steps variations of low and high cutoff frequencies of a digital bandpass filter. Teager-Kaiser Energy operator, root mean square and simple threshold method were applied for both techniques. Results show a narrowing of the effective bandwidth vs. the literature recommended parameters for sEMG acquisition. Both level 3 decomposition with mother wavelet db4 and bandpass filter with cutoff frequencies between 130 and 180 Hz were optimal for onset detection in infrahyoid muscles. The proposed methodologies recognized the onset time with predictive power above 0.95, that is similar to previous findings but in larger and more superficial muscles in limbs.     

Improvement of protein secondary structure prediction using binary word encoding

We propose a binary word encoding to improve the protein secondary structure prediction. A binary word encoding encodes a local amino acid sequence to a binary word, which consists of 0 or 1. We use an encoding function to map an amino acid to 0 or 1. Using the binary word encoding, we can statistically extract the multiresidue information, which depends on more than one residue. We combine the binary word encoding with the GOR method, its modified version, which shows better accuracy, and the neural network method. The binary word encoding improves the accuracy of GOR by 2.8%. We obtain similar improvement when we combine this with the modified GOR method and the neural network method. When we use multiple sequence alignment data, the binary word encoding similarly improves the accuracy. The accuracy of our best combined method is 68.2%. In this paper, we only show improvement of the GOR and neural network method, we cannot say that the encoding improves the other methods. But the improvement by the encoding suggests that the multiresidue interaction affects the formation of secondary structure. In addition, we find that the optimal encoding function obtained by the simulated annealing method relates to non-polarity. This means that nonpolarity is important to the multiresidue interaction. Proteins 27:36–46 © 1997 Wiley-Liss, Inc.