Reliability of surface EMG during sustained contractions of the quadriceps.

The purpose of this study was to determine test-retest reliability for median frequency (MDF) and amplitude of surface EMG during sustained fatiguing contractions of the quadriceps. Twenty-two healthy subjects (11 males and 11 females) were tested on two days held one week apart. Surface EMG was recorded from rectus femoris (RF), vastus lateralis (VL) and vastus medialis (VM) during sustained isometric contractions at 80% and 20% of maximal voluntary contraction (MVC) held to exhaustion. Quadriceps fatigue was described using four measures for both MDF and amplitude of EMG: initial, final, normalized final and slope. For both MDF and amplitude, the initial, final and normalized EMG showed moderate to high reliability for all three muscle groups at both contraction levels (ICC=0.59-0.88 for MDF; ICC=0.58-0.99 for amplitude). Slope of MDF and amplitude was associated with a large degree of variability and low ICCs for the 80% but not the 20% MVC. MDF and amplitude of EMG during sustained contractions of the quadriceps are reproducible; normalized final values of MDF and amplitude show better reliability than slope.     

Reliability of EMG spectral parameters in repeated measurements of back muscle fatigue.

The change in median frequency of the power spectrum of the electromyographic (EMG) signal may be used as a measure of muscle fatigue. The reliability of the median frequency parameters was investigated for EMG-recording sites at L1 and L5 right and left on the erector spinae. The reliability of subjective fatigue ratings of the back muscles (Borg CR-10 scale) and of maximal trunk extension torque (MVC) was also investigated. Eleven subjects with healthy backs performed a 45-s isometric trunk extension at 80% of MVC twice a day, on three different days. Two-factor analysis of variance was made to obtain the different variances from which the standard error of measurement (SEM) and the intra class correlation coefficient (ICC) were calculated. The SEM within-day was somewhat lower than that between-days. Both were about the same at all four electrode sites. The 95% confidence interval for the studied variables was for the initial median frequency +/- 10 Hz, for the slope +/- 0.4-0.5%/s, for the MVC +/- 36 Nm and for the Borg ratings +/- 1.6. We conclude that, with the presently used method, changes or differences within these limits should be regarded as normal variability. The slope may be of limited value because of its large variability. Whether the low intraclass correlation coefficient for the EMG parameters in the presently studied test group implies a low potential in discriminating subjects with back pain can not be decisively concluded.     

Predicting maximum eccentric strength from surface EMG measurements

The origin of the well-documented discrepancy between maximum voluntary and in vitro tetanic eccentric strength has yet to be fully understood. This study aimed to determine whether surface EMG measurements can be used to reproduce the in vitro tetanic force–velocity relationship from maximum voluntary contractions. Five subjects performed maximal knee extensions over a range of eccentric and concentric velocities on an isovelocity dynamometer whilst EMG from the quadriceps were recorded. Maximum voluntary (MVC) force–length–velocity data were estimated from the dynamometer measurements and a muscle model. Normalised amplitude–length–velocity data were obtained from the EMG signals. Dividing the MVC forces by the normalised amplitudes generated EMG corrected force–length–velocity data. The goodness of fit of the in vitro tetanic force–velocity function to the MVC and EMG corrected forces was assessed. Based on a number of comparative scores the in vitro tetanic force–velocity function provided a significantly better fit to the EMG corrected forces compared to the MVC forces (p?0.05), Furthermore, the EMG corrected forces generated realistic in vitro tetanic force–velocity profiles. A 58±19% increase in maximum eccentric strength is theoretically achievable through eliminating neural factors. In conclusion, EMG amplitude can be used to estimate in vitro tetanic forces from maximal in vivo force measurements, supporting neural factors as the major contributor to the difference between in vitro and in vivo maximal force.     

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.     

Investigations of back muscle fatigue by simultaneous 31P MRS and surface EMG measurements.

Investigations of back muscle fatigue are important for understanding the role of muscle strain in the development of low back pain. The aim of this contribution is to review the two main techniques used for in vivo investigations of metabolic and electrophysiological changes, namely magnetic resonance phosphorous spectroscopy ((31)P MRS) and surface electromyography (SEMG), and to report some of our recent results on simultaneous measurements using these techniques during isometric back-muscle contraction in volunteers. Since it appears that electrophysiological and metabolic factors are simultaneously involved in the processes of fatigue and muscle recovery during load application, simultaneous acquisition of complete information is quite promising for obtaining new insights into the metabolic origin of electrophysiological changes or vice versa. Performing these measurements simultaneously, however, is more intricate owing to the occurrence of signal artifacts caused by mutual signal interferences of both techniques. Besides these mutual disturbances, further experimental difficulties are related to spatial limitations within the bore of clinical whole-body high-field magnetic resonance (MR) systems (1.5 T) and the sensitivity of MR measurements to motion-induced artifacts. Our own experimental results are presented, and problems that occur using both techniques simultaneously, as well as possibilities to resolve them, are discussed. The results shed light on the interrelation of electrophysiological and metabolic changes during fatigue of the back muscle while performing an exercise.     

Reliability of surface EMG matrix in locating the innervation zone of upper trapezius muscle

The identification of the motor unit (MU) innervation zone (IZ) using surface electromyographic (sEMG) signals detected on the skin with a linear array or a matrix of electrodes has been recently proposed in the literature. However, an analysis of the reliability of this procedure and, therefore, of the suitability of the sEMG signals for this purpose has not been reported.The purpose of this work is to describe the intra and inter-rater reliability and the suitability of surface EMG in locating the innervation zone of the upper trapezius muscle.Two operators were trained on electrode matrix positioning and sEMG signal analysis. Ten healthy subjects, instructed to perform a series of isometric contractions of the upper trapezius muscle participated in the study. The two operators collected sEMG signals and then independently estimated the IZ location through visual analysis.Results showed an almost perfect agreement for intra-rater and inter-rater reliability. The constancy of IZ location could be affected by the factors reflecting the population of active MUs and their IZs, including: the contraction intensity, the acquisition period analyzed, the contraction repetition. In almost all cases the IZ location shift due to these factors did not exceed 4 mm. Results generalization to other muscles should be made with caution.     

Reliability of EMG measurements for trunk muscles during maximal and sub-maximal voluntary isometric contractions in healthy controls and CLBP patients.

The purpose of this study was to compare the reliability of trunk muscle activity measured by means of surface electromyography (EMG) during maximal and sub-maximal voluntary isometric contractions (MVC/sub-MVC) over repeated trials within-day and between-days in healthy controls and patients with chronic low back pain (CLBP). Eleven volunteers (six controls and five CLBP patients) were assessed twice with a 1-week interval. Surface EMG signals were recorded bilaterally from six trunk muscles. Intra-class correlation coefficients (ICC) and standard error of measurement as a percentage of the grand mean (%SEM) were calculated. MVC and sub-MVC showed excellent within-day reliability in both healthy controls and CLBP patients (ICC mean 0.91; range 0.75-0.98; %SEM mean 4%; range 1-12%). Sub-MVC for both groups between-days showed excellent reliability (ICC mean 0.88; range 0.78-0.97; %SEM mean 7%; range 3-11%). The between-days MVC for both groups showed trends towards lower levels of reliability (ICC mean 0.70; range 0.19-0.99; %SEM mean 17%; range 4-36%) when compared to sub-MVC. Findings of the study provide evidence that sub-MVC are preferable for amplitude normalisation when assessing EMG signals of trunk muscles between-days.     

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.     

Analysis of EMG measurements during bicycle pedalling

Activity of eight leg muscles has been monitored for six test subjects while pedalling a bicycle on rollers in the laboratory. Each electromyogram (EMG) data channel was digitized at a sampling rate of 2 kHz by a minicomputer. Data analysis entailed generating plots of both EMG activity regions and integrated EMG (IEMG). For each test subject, data were recorded for five cases of pedalling conditions. The different pedalling conditions were defined to explore a variety of research hypotheses. This exploration has led to the following conclusions: Muscular activity levels of the quadriceps are influenced by the type of shoes worn and activity levels increase with soft sole shoes as opposed to cycling shoes with cleats and toeclips. EMG activity patterns are not strongly related to pedalling conditions (i.e. load, seat height and shoe type). The level of muscle activity, however, is significantly affected by pedalling conditions. Muscular activity bears a complex relationship with seat height and quadriceps activity level decreases with greater seat height. Agonist (i.e. hamstrings) and antagonist (i.e. quadriceps) muscles of the hip/knee are active simultaneously during leg extension. Regions of peak activity levels, however, do not overlap. The lack of significant cocontraction of agonist/antagonist muscles enables muscle forces during pedalling action to be computed by solving a series of equilibrium problems over different regions of the crank cycle. Regions are defined and a solution procedure is outlined.     

Theoretical analysis of surface EMG in voluntary isometric contraction.

A new stochastic model of the surface EMG is suggested and the spectral density of the surface EMG is studied theoretically and experimentally to confirm the validity of this model. Theoretical results show that while the contraction level is not so high, the shape of the spectral density (distribution) does not change and its amplitude is directly proportional to the motor unit firing frequency and recruitment. To illustrate the theoretical results, experiments were carried out for rectus femoris and biceps brachii. The surface EMG was lead off by bipolar surface electrodes. And the spectral density of the surface EMG was calculated using FFT algorithm. From these experimental results, it was confirmed that our theoretical results were almost valid.     

Digital filter design for peak detection of surface EMG.

A simple Low-Pass Differential (LPD) filter is often used for the Motor Unit Action Potential (MUAP) peak detection in needle EMG decomposition. Decomposition of surface EMG is much more difficult since surface EMG is more mixed than needle EMG. A successful peak detection of MUAPs is a first important step for EMG decomposition. We found that a simple LPD filter is not suitable for surface MUAP detection; instead the Weighted Low-Pass Differential (WLPD) filters are proposed. The filter performance is analyzed based on different window selection and varying MUAPs from simulated and recorded surface EMG. The sinusoidal WLPD filter is found to have better SNR improving factors, and to be more robust under the varying conditions.     

The extraction of neural strategies from the surface EMG.

This brief review examines some of the methods used to infer central control strategies from surface electromyogram (EMG) recordings. Among the many uses of the surface EMG in studying the neural control of movement, the review critically evaluates only some of the applications. The focus is on the relations between global features of the surface EMG and the underlying physiological processes. Because direct measurements of motor unit activation are not available and many factors can influence the signal, these relations are frequently misinterpreted. These errors are compounded by the counterintuitive effects that some system parameters can have on the EMG signal. The phenomenon of crosstalk is used as an example of these problems. The review describes the limitations of techniques used to infer the level of muscle activation, the type of motor unit recruited, the upper limit of motor unit recruitment, the average discharge rate, and the degree of synchronization between motor units. Although the global surface EMG is a useful measure of muscle activation and assessment, there are limits to the information that can be extracted from this signal.     

The methods of surface EMG analysis

The use of surface emg as a tool for quantification is described. First, the specific advantages of the surface emg are discussed. Techniques for analysis of the emg signal which estimate and detect the action potentials of the individual motor units and estimate some global properties of muscle activity are reviewed. A survey of data on relations between the properties of motor unit action potential, the properties of motor unit activity and the results of signal processing are given.     

A segmentation approach to long duration surface EMG recordings.

The purpose of this study was to develop an automatic segmentation method in order to identify postural surface EMG segments in long-duration recordings. Surface EMG signals were collected from the cervical erector spinae (CES), erector spinae (ES), external oblique (EO), and tibialis anterior (TA) muscles of 11 subjects using a bipolar electrode configuration. Subjects remained seated in a car seat over the 150-min data-collection period. The modified dynamic cumulative sum (MDCS) algorithm was used to automatically segment the surface EMG signals. Signals were rejected by comparison with an exponential mathematical model of the spectrum of a surface EMG signal. The average power ratio computed between two successive retained segments was used to classify segments as postural or surface EMG. The presence of a negative slope of a regression line fitted to the median frequency values of postural surface EMG segments was taken as an indication of fatigue. Alpha level was set at 0.05. The overall classification error rate was 8%, and could be performed in 25 min for a 150-min signal using a custom-built software program written in C (Borland Software Corporation, CA, USA). This error rate could be enhanced by concentrating on the rejection method, which caused most of the misclassification (6%). Furthermore, the elimination of non-postural surface EMG segments by the use of a segmentation approach enabled muscular fatigue to be identified in signals that contained no evidence of fatigue when analysed using traditional methods.     

Prediction of handgrip forces using surface EMG of forearm muscles.

Evaluation of handgrip forces constitutes an essential component of ergonomic evaluation (e.g. of hand tools), but is difficult to perform at the workplace. The present study describes a series of experiments on 8 healthy male subjects to determine the validity of linear regression models using the surface electromyography (EMG) of up to 6 forearm muscles to predict handgrip forces. For isometric gripping tasks, normalized EMG to grip force calibrations using a series of dynamic force bursts up to 300 N resulted in a valid prediction of grip forces based on the EMG of 6 forearm muscles. Absolute differences between observed and predicted grip force were small (between 27 and 41 N) which shows that the proposed method might be used for the ergonomic evaluation of the use of hand tools. The EMG - handgrip force model appeared to be minimally affected by grip width, i.e. a model for 67 mm grip width was able to validly predict grip forces for 59 and 75 mm grip widths. Furthermore, it was shown that of the 6 forearm muscles studied at least 3 have to be assessed to arrive at a sufficient level of validity, while it seems to be irrelevant which 3 of those 6 forearm muscles are assessed.     

EMG muscle scanning: comparison to attached surface electrodes.

Electromyographic (EMG) muscle scanning measures brief samples of integrated muscle action potentials from individual muscles using a hand-held scanner with post-style electrodes. This "scanning" technique is widely used by biofeedback practitioners to quickly assess muscle activity in the diagnosis of musculoskeletal disorders. In an effort to compare muscle scanning with the established technique using attached surface electrodes, ten healthy subjects (25-35 years old) were scanned using 2-second sampling at five bilateral muscle sites while simultaneously monitoring the same sites with surface electrodes. This was repeated using 10-second scanning samplings. Pearson's product-moment correlations between scanning for 2 seconds and prolonged surface recording at all sites were 0.54-0.89. Scanning for 10 seconds improved the correlations to 0.68-0.91. EMG scanning for 2 seconds compares favorably with attached surface electrode recording. Comparisons are further improved by 10-second scans.     

Adaptive filtering for ECG rejection from surface EMG recordings.

Surface electromyograms (EMG) of back muscles are often corrupted by electrocardiogram (ECG) signals. This noise in the EMG signals does not allow to appreciate correctly the spectral content of the EMG signals and to follow its evolution during, for example, a fatigue process. Several methods have been proposed to reject the ECG noise from EMG recordings, but seldom taking into account the eventual changes in ECG characteristics during the experiment. In this paper we propose an adaptive filtering algorithm specifically developed for the rejection of the electrocardiogram corrupting surface electromyograms (SEMG). The first step of the study was to choose the ECG electrode position in order to record the ECG with a shape similar to that found in the noised SEMGs. Then, the efficiency of different algorithms were tested on 28 erector spinae SEMG recordings. The best algorithm belongs to the fast recursive least square family (FRLS). More precisely, the best results were obtained with the simplified formulation of a FRLS algorithm. As an application of the adaptive filtering, the paper compares the evolutions of spectral parameters of noised or denoised (after adaptive filtering) surface EMGs recorded on erector spinae muscles during a trunk extension. The fatigue test was analyzed on 16 EMG recordings. After adaptive filtering, mean initial values of energy and of mean power frequency (MPF) were significantly lower and higher respectively. The differences corresponded to the removal of the ECG components. Furthermore, classical fatigue criteria (increase in energy and decrease in MPF values over time during the fatigue test) were better observed on the denoised EMGs. The mean values of the slopes of the energy-time and MPF-time linear relationships differed significantly when established before and after adaptive filtering. These results account for the efficacy of the adaptive filtering method proposed here to denoise electrophysiological signals.