The present study aimed at assessing the test-retest reliability of wavelet - and Fourier derived (instantaneous) median frequencies of surface electromyographic (EMG) measurements of back and hip muscles during isometric back extensions. Twenty healthy subjects (10 males and 10 females) performed a modified Biering-Sørensen test on two separate days, with a 1-week interval between the two tests. Surface EMG measurements were bilaterally performed from the latissimus dorsi, the thoracic and lumbar parts of the longissimus thoracis, the thoracic and lumbar parts of the iliocostalis lumborum, the multifidus, the gluteus maximus and the biceps femoris. In addition, three-dimensional kinematic data were recorded of the subjects' lumbar vertebrae. The (instantaneous) median frequencies were calculated from the EMG signals using continuous wavelet (IMDF) - and short-time Fourier transforms (MDF). Linear regressions performed on the IMDF and MDF data as a function of time yielded slopes (IMDF(slope) and MDF(slope)) and intercepts (IMDF(init) and MDF(init)) of the regression lines. Test-retest reliability was assessed on the normalized slopes and intercept parameters by means of intraclass correlation coefficients (ICC) and standard errors of measurements expressed as percentages of the mean values (% SEM). The results of IMDF(slope) and MDF(slope) parameters indicated ICCs for back and hip muscles between .443 and .727 for IMDF(slope), values between .273 and .734 for MDF(slope), % SEM between 7.6% and 58.9% for IMDF(slope) and % SEM between 8.2% and 25.3% for MDF(slope), respectively. The ICCs for IMDF(init) and MDF(init) parameters varied between .376 and .907 for IMDF(init) and between .383 and .883 for MDF(init), and % SEM ranged from 2.7% to 6.3% for IMDF(init) and from 2.6% to 4.7% for MDF(init), respectively. These results indicate that both wavelet - and Fourier based (instantaneous) median frequency parameters generally are reliable in the analysis of back and hip muscle fatigue during a modified Biering-Sørensen test.
BACKGROUND: fibromyalgia is a relatively common condition with widespread pain and pressure allodynia, but unknown aetiology. For decades, the association between motor control strategies and chronic pain has been a topic for debate. One long held functional neuromuscular control mechanism is differential activation between regions within a single muscle. The aim of this study was to investigate differences in neuromuscular control, i.e. differential activation, between myalgic trapezius in fibromyalgia patients and healthy controls. METHODS: 27 fibromyalgia patients and 30 healthy controls performed 3 minutes bilateral shoulder elevations with different loads (0-4 Kg) with a high-density surface electromyographical (EMG) grid placed above the upper trapezius. Differential activation was quantified by the power spectral median frequency of the difference in EMG amplitude between the cranial and caudal parts of the upper trapezius. The average duration of the differential activation was described by the inverse of the median frequency of the differential activations. RESULTS: the median frequency of the differential activations was significantly lower, and the average duration of the differential activations significantly longer in fibromyalgia compared with controls at the two lowest load levels (0-1 Kg) (p < 0.04), but not at the two highest load levels (2 and 4 Kg). CONCLUSION: these findings illustrate a different neuromuscular control between fibromyalgia patients and healthy controls during a low load functional task, either sustaining or resulting from the chronic painful condition. The findings may have clinical relevance for rehabilitation strategies for fibromyalgia.
Fibromyalgia is a common chronic pain condition in the population (2-4%), which often is associated with prominent negative consequences with respect to participation in daily activities. There are several reports in the literature concerning the effects of acute experimental pain on motor control. However, a more heterogeneous picture exists in the literature with respect to whether chronic pain conditions affect motor control. This study compares firing rate and conduction velocity (CV) of single motor units (MUs) in the trapezius muscle of fibromyalgia patients (FM) and healthy controls (CON). Multi-channel surface electromyography was used to estimate both MU firing rate and CV because this technique allows simultaneous estimation of both these variables and the measurements are easy and non-invasive. In this study, 29 FM and 30 CON subjects participated and performed isometric shoulder elevations using weights up to 4 kg. No significant differences in the firing rate of MUs in the trapezius muscle were found between the FM and CON groups (95% confidence interval was -1.9 and 1.3 pulses per second). There were no significant differences in CV between the groups at 1 and 2 kg load. However, the FM group had significantly higher CV in contractions without external load (p=0.004). We were unable to confirm the pain-adaptation model since no differences in firing rate between the two groups were found. CV was significantly higher in FM than in healthy controls; this might be due to alterations in histopathology and microcirculation.
The helical axis model can be used to describe translation and rotation of spine segments. The aim of this study was to investigate the cervical helical axis and its center of rotation during fast head movements (side rotation and flexion/extension) and ball catching in patients with non-specific neck pain or pain due to whiplash injury as compared with matched controls. The aim was also to investigate correlations with neck pain intensity. A finite helical axis model with a time-varying window was used. The intersection point of the axis during different movement conditions was calculated. A repeated-measures ANOVA model was used to investigate the cervical helical axis and its rotation center for consecutive levels of 15° during head movement. Irregularities in axis movement were derived using a zero-crossing approach. In addition, head, arm and upper body range of motion and velocity were observed. A general increase of axis irregularity that correlated to pain intensity was observed in the whiplash group. The rotation center was superiorly displaced in the non-specific neck pain group during side rotation, with the same tendency for the whiplash group. During ball catching, an anterior displacement (and a tendency to an inferior displacement) of the center of rotation and slower and more restricted upper body movements implied a changed movement strategy in neck pain patients, possibly as an attempt to stabilize the cervical spine during head movement.
The helical axis model can be used to describe translation and rotation of spine segments. The aim of this study was to investigate the cervical helical axis and its center of rotation during fast head movements (side rotation and flexion/extension) and ball catching in patients with non-specific neck pain or pain due to whiplash injury as compared with matched controls. The aim was also to investigate correlations with neck pain intensity. A finite helical axis model with a time-varying window was used. The intersection point of the axis during different movement conditions was calculated. A repeated-measures ANOVA model was used to investigate the cervical helical axis and its rotation center for consecutive levels of 15 degrees during head movement. Irregularities in axis movement were derived using a zero-crossing approach. In addition, head, arm and upper body range of motion and velocity were observed. A general increase of axis irregularity that correlated to pain intensity was observed in the whiplash group. The rotation center was superiorly displaced in the non-specific neck pain group during side rotation, with the same tendency for the whiplash group. During ball catching, an anterior displacement (and a tendency to an inferior displacement) of the center of rotation and slower and more restricted upper body movements implied a changed movement strategy in neck pain patients, possibly as an attempt to stabilize the cervical spine during head movement.
This paper presents a new method for classification of neck movement patterns related to Whiplash-associated disorders (WAD) using a resilient backpropagation neural network (BPNN). WAD are a common diagnosis after neck trauma, typically caused by rear-end car accidents. Since physical injuries seldom are found with present imaging techniques, the diagnosis can be difficult to make. The active range of the neck is often visually inspected in patients with neck pain, but this is a subjective measure, and a more objective decision support system, that gives a reliable and more detailed analysis of neck movement pattern, is needed. The objective of this study was to evaluate the predictive ability of a BPNN, using neck movement variables as input. Three-dimensional (3-D) neck movement data from 59 subjects with WAD and 56 control subjects were collected with a ProReflex system. Rotation angle and angle velocity were calculated using the instantaneous helical axis method and motion variables were extracted. A principal component analysis was performed in order to reduce data and improve the BPNN performance. BPNNs with six hidden nodes had a predictivity of 0.89, a sensitivity of 0.90 and a specificity of 0.88, which are very promising results. This shows that neck movement analysis combined with a neural network could build the basis of a decision support system for classifying suspected WAD, even though further evaluation of the method is needed.
Motor unit (MU) synchronization is the result of commonality in the pre-synaptic input to MUs. Previously proposed techniques to estimate MU synchronization based on invasive and surface electromyography (sEMG) recordings have been, respectively, limited by the analyzed MU population size and influence of changes in muscle fibre conduction velocities (MFCVs). The aim of this paper was to evaluate a novel descriptor of MU synchronization on a large MU population, and to minimize its dependency on MFCV. The method is based on the asymmetry of MU action potentials, causing synchronized MU action potentials to skew the monopolar sEMG signal distribution. The descriptor was the skewness statistic used on sub-band filtered monopolar sEMG signals (sub-band skewness). The method was evaluated using simulated signals and its performance was evaluated in terms of bias and sensitivity of the sub-band skewness quantifying the MU synchronization level. The best sensitivity was obtained using sub-band filtering at scale 5 (Mexican hat wavelet). The sensitivity was in general about 0.1units per 5% MU synchronization level. Changes in MFCV had a minimal influence, and caused at most a 5% deviant MU synchronization quantification level. A halved recruitment level had higher bias and a 20% lower sensitivity. Increased firing rate (14-34Hz) reduced the sensitivity about 50%. The sensitivity of the descriptor was robust to noise, and different volume conduction properties. It should be noted that the sub-band skewness comprises a subject-dependent component implying that only changes in MU synchronization level can be quantified.
Radiation therapy causes both muscle and nerve tissue damage. However, the evolution and mechanisms of these damages are not fully understood. Information on the state of active muscle fibres and motoneurons can be obtained by measuring sEMG signals and calculating the conduction velocity (CV) and firing rate of individual motor units, respectively. The aim of this pilot study was to evaluate if the multi-channel surface EMG (sEMG) technique could be applied to the sternocleidomastoideus muscle (SCM) of radiotherapy patients, and to assess if the CV and firing rate are altered as a consequence of the radiation.
Surface EMG signals were recorded from the radiated and healthy SCM muscles of 10 subjects, while subjects performed isometric rotation of the head. CV and firing rate were calculated using two recently proposed methods based on spatio-temporal processing of the sEMG signals. The multi-channel sEMG technique was successfully applied to the SCM muscle and CV and firing rates were obtained. The measurements were fast and simple and comfortable for the patients. Sufficient data quality was obtained from both sides of seven and four subjects for the CV and firing rate analysis, respectively. No differences in CV or firing rate were found between the radiated and non-radiated sides (p = 0.13 and p = 0.20, respectively). Firing rate and CV were also obtained from a myokymic discharge pattern. It was found that the CV decreased significantly (p = 0.01) during the bursts.
The aim of this study was to investigate the importance of duration of differential activations between the heads of the biceps brachii on local fatigue during prolonged low-level contractions. Fifteen subjects carried out isometric elbow flexion at 5% of maximal voluntary contraction (MVC) for 30 min. MVCs were performed before and at the end of the prolonged contraction. Surface electromyographic (EMG) signals were recorded from both heads of the biceps brachii. Differential activation was analysed based on the difference in EMG amplitude (activation) between electrodes situated at the two heads. Differential activations were quantified by the power spectral median frequency of the difference in activation between the heads throughout the contraction. The inverse of the median frequency was used to describe the average duration of the differential activations. The relation between average duration of the differential activations and the fatigue-induced reduction in maximal force was explored by linear regression analysis. The main finding was that the average duration of differential activation was positively associated to relative maximal force at the end of the 30 min contraction (R(2)=0.5, P<0.01). The findings of this study highlight the importance of duration of differential activations for local fatigue, and support the hypothesis that long term differential activations prevent fatigue during prolonged low-level contractions.
Aim: To examine the occurrence of repeated differential activation between the heads of the biceps brachii muscle and its relation to fatigue prevention during a submaximal contraction.
Methods: Thirty‐nine subjects carried out an isometric contraction of elbow flexion at 25% of maximal voluntary contraction (MVC) until exhaustion. A grid of 13 by 10 electrodes was used to record surface electromyographic signals from both heads of the biceps brachii. The root‐mean‐square of signals recorded from electrodes located medially and laterally was used to analyse activation differences. Differential activation was defined as periods of 33% different activation level between the two heads of the biceps brachii muscle.
Results: Differential muscle activation was demonstrated in 30 of 33 subjects with appropriate data quality. The frequency of differential activation increased from 4.9 to 6.6 min−1 at the end of the contractions with no change in duration of the differential activations (about 1.4 s). Moreover, the frequency of differential activation was, in general, negatively correlated with time to exhaustion.
Conclusion: The observed differential activation between the heads of the biceps brachii can be explained by an uneven distribution of synaptic input to the motor neurone pool. The findings of this study indicate that differential activation of regions within a muscle does not prevent fatigue at a contraction level of 25% of MVC.
The amount of documented increase in motor unit (MU) synchronization with fatigue and its possible relation with force tremor varies largely, possibly due to inhomogeneous muscle activation and methodological discrepancies and limitations. The aim of this study was to apply a novel surface electromyographical (EMG) descriptor for MU synchronization based on large MU populations to examine changes in MU synchronization with fatigue at different sites of a muscle and its relation to tremor. Twenty-four subjects performed an isometric elbow flexion at 25% of maximal voluntary contraction until exhaustion. Monopolar EMG signals were recorded using a grid of 130 electrodes above the biceps brachii. Changes in MU synchronization were estimated based on the sub-band skewness of EMG signals and tremor by the coefficient of variation in force. The synchronization descriptor was dependent on recording site and increased with fatigue together with tremor. There was a general association between these two parameters, but not between their fluctuations. These results are in agreement with other surface EMG studies and indicate that the novel descriptor can be used to attain information of synchronization between large MU populations during fatigue that cannot be retrieved with intra-muscular EMG.
The aim of this study was to provide direct in vivo information of the physiological and structural characteristics of active muscle fibres from a large part of the upper trapezius muscle. Two-dimensional (2-D) multi-channel surface electromyography recordings were used, with 13 × 10 electrodes covering 6 × 4.5 cm of the skin’s surface. A previously developed method was applied to detect individual propagating motor unit action potentials and to estimate their corresponding muscle fibre conduction velocity (MFCV) and muscle fibre orientation (MFO). Using these estimates, spatial distributions of MFCV and MFO were examined for five male subjects performing isometric shoulder elevation at different force levels. The main results were: (1) the general relationship between MFCV and force generation was non-systematic, with a positive relationship at the inferior part of the muscle, (2) the spatial distribution of MFCV at different force levels and fatigue was inhomogeneous and (3) the MFO was slightly different (6°) of the muscle fibres with origin superior compared to inferior to the C7 vertebra. These findings provide new information of the MFO of contracting muscle fibres and knowledge of the physiological characteristics of a large part of the upper trapezius muscle that previously was based on observations from human cadavers only.
Task-dependent differences in relative activity between "functional" subdivisions within human muscles are well documented. Contrary, independent voluntary control of anatomical subdivisions, termed neuromuscular compartments is not observed in human muscles. Therefore, the main aim of this study was to investigate whether subdivisions within the human trapezius can be independently activated by voluntary command using biofeedback guidance. Bipolar electromyographical electrodes were situated on four subdivisions of the trapezius muscle. The threshold for "active" and "rest" for each subdivision was set to >12% and <1.5% of the maximal electromyographical amplitude recorded during a maximal voluntary contraction. After 1h with biofeedback from each of the four trapezius subdivisions, 11 of 15 subjects learned selective activation of at least one of the four anatomical subdivisions of the trapezius muscle. All subjects managed to voluntarily activate the lower subdivisions independently from the upper subdivisions. Half of the subjects succeeded to voluntarily activate both upper subdivisions independently from the two lower subdivisions. These findings show that anatomical subdivisions of the human trapezius muscle can be independently activated by voluntary command, indicating neuromuscular compartmentalization of the trapezius muscle. The independent activation of the upper and lower subdivisions of the trapezius is in accordance with the selective innervation by the fine cranial and main branch of the accessory nerve to the upper and lower subdivisions. These findings provide new insight into motor control characteristics, learning possibilities, and function of the clinically relevant human trapezius muscle.
A surface electromyogram (sEMG) contains information about physiological and morphological characteristics of the active muscle and its neural strategies. Because the electrodes are situated on the skin above the muscle, the sEMG is an easily obtainable source of information. However, different combinations of physiological and morphological characteristics can lead to similar sEMG signals and sEMG recordings contain noise and other artefacts. Therefore, many sEMG signal processing methods have been developed and applied to allow insight into neuromuscular physiology. This paper gives an overview of important advances in the development and applications of sEMG signal processing methods, including spectral estimation, higher order statistics and spatio-temporal processing. These methods provide information about muscle activation dynamics and muscle fatigue, as well as characteristics and control of single motor units (conduction velocity, firing rate, amplitude distribution and synchronization).
A new method is proposed, based on the pole phase angle (PPA) of a second-order autoregressive (AR) model, to track spectral alteration during localised muscle fatigue when analysing surface myo-electric (ME) signals. Both stationary and non-stationary, simulated and real ME signals are used to investigate different methods to track spectral changes. The real ME signals are obtained from three muscles (the right vastus lateralis, rectus femoris and vastus medialis) of six healthy male volunteers, and the simulated signals are generated by passing Gaussian white-noise sequences through digital filters with spectral properties that mimic the real ME signals. The PPA method is compared, not only with spectra-based methods, such as Fourier and AR, but also with zero crossings (ZCs) and the first AR coefficient that have been proposed in the literature as computer efficient methods. By comparing the deviation (dev), in percent, between the linear regression of the theoretical and estimated mean frequencies of the power spectra for simulated stationary (s) and non-stationary (ns) signals, in general, it is found that the PPA method (dev(s) = 4.29; dev(ns) = 1.94) gives a superior performance to ZCs (dv(s) = 8.25) and the first AR coefficient (4.18<dev(s)<21.8; 0.98<dev(ns)<4.36) but performs slightly worse than spectra-based methods (0.33<dev(s)<0.79; 0.41<dev(ns)<1.07). However, the PPA method has the advantage that it estimates spectral alteration without calculating the spectra and therefore allows very efficient computation.
In this paper, we introduce the nonstationary signal analysis methods to analyze the myoelectric (ME) signals during dynamic contractions by estimating the time-dependent spectral moments. The time-frequency analysis methods including the short-time Fourier transform, the Wigner–Ville distribution, the Choi–Williams distribution, and the continuous wavelet transform were compared for estimation accuracy and precision on synthesized and real ME signals. It is found that the estimates providedby the continuous wavelet transform have better accuracy and precision than those obtained with the other time-frequency analysis methods on simulated data sets. In addition, ME signals from four subjects during three different tests (maximum static voluntary contraction, ramp contraction, and repeated isokinetic contractions) were also examined.
In this paper, we introduce wavelet packets as an alternative method for spectral analysis of surface myoelectric(ME) signals. Both computer synthesized and real ME signals are used to investigate the performance. Our simulation results show that wavelet packet estimate has slightly less mean squareerror (MSE) than Fourier method, and both methods perform similarly on the real data. Moreover, wavelet packets give us some advantages over the traditional methods such as multiresolutionof frequency, as well as its potential use for effecting time-frequency decomposition of the nonstationary signals such as the ME signals during dynamic contractions. We also introduce wavelet shrinkage method for improving spectral estimates bysignificantly reducing the MSE’s for both Fourier and wavelet packet methods.
Wavelet packets are a useful extension of wavelets, which are of wide potential use in a statistical context. In this paper, an approach to the local spectral analysis of a stationary time series based on wavelet packet decomposition is developed. This involves extensions to the wavelet context of standard time series ideas such as the periodogram and spectrum. Some asymptotic properties of the new estimate are provided. The technique is illustrated by simulated signals and its application to physiological data, and its potential use in studies of time-dependent spectral analysis is discussed.
Exposure to vibration is suggested as a risk factor for developing neck and shoulder disorders in working life. Mechanical vibration applied to a muscle belly or a tendon can elicit a reflex muscle contraction, also called tonic vibration reflex, but the mechanisms behind how vibration could cause musculoskeletal disorders has not yet been described. One suggestion has been that the vibration causes muscular fatigue. This study investigates whether vibration exposure changes the development of muscular fatigue in the trapezius muscle. Thirty-seven volunteers (men and women) performed a sub-maximal isometric shoulder elevation for 3min. This was repeated four times, two times with induced vibration and two times without. Muscle activity was measured before and after each 3-min period to look at changes in the electromyography parameters. The result showed a significantly smaller mean frequency decrease when performing the shoulder elevation with vibration (-2.51Hz) compared to without vibration (-4.04Hz). There was also a slightly higher increase in the root mean square when exposed to vibration (5.7% of maximal voluntary contraction) compared to without (3.8% of maximal voluntary contraction); however, this was not statistically significant. The results of the present study indicate that short-time exposure to vibration has no negative acute effects on the fatiguing of upper trapezius muscle.
This paper presents an assessment tool for objective neck movement analysis of subjects suffering from chronic whiplash-associated disorders (WAD). Three-dimensional (3-D) motion data is collected by a commercially available motion analysis system. Head rotation, defined in this paper as the rotation angle around the instantaneous helical axis (IHA), is used for extracting a number of variables (e.g., angular velocity and range, symmetry of motion). Statistically significant differences were found between controls and subjects with chronic WAD in a number of variables.
In this paper we show how independent component analysis (ICA) algorithms can be used to perform spatio-temporal filtration of electromyographic (EMG) and electrocardiographic (ECG) signals. The technique was used to decompose the EMG signals into motor unit action potential (MUAP) trains. From the 88 outputs of the adaptive spatio-temporal filtration, three groups of different MUAP train patterns were found. The technique was also used to obtain a fetus' ECG and showed better result compared to using ICA.
A motor unit (MU) is defined as an anterior horn cell, its axon, and the muscle fibres innervated by the motor neuron. A surface electromyogram (EMG) is a superposition of many different MU action potentials (MUAPs) generated by active MUs. The objectives of this study were to introduce a new adaptive spatio-temporal filter, here called maximum kurtosis filter (MKF), and to compare it with existing filters, on its performance to detect a single MUAP train from multichannel surface EMG signals. The MKF adaptively chooses the filter coefficients by maximising the kurtosis of the output. The proposed method was compared with five commonly used spatial filters, the weighted low-pass differential filter (WLPD) and the marginal distribution of a continuous wavelet transform. The performance was evaluated using simulated EMG signals. In addition, results from a multichannel surface EMG measurement fro from a subject who had been previously exposed to radiation due to cancer were used to demonstrate an application of the method. With five time lags of the MKF, the sensitivity was 98.7% and the highest sensitivity of the traditional filters was 86.8%, which was obtained with the WLPD. The positive predictivities of these filters were 87.4 and 80.4%, respectively. Results from simulations showed that the proposed spatio-temporal filtration technique significantly improved performance as compared with existing filters, and the sensitivity and the positive predictivity increased with an increase in number of time lags in the filter.
Spatial filtering of surface electromyography (EMG) signals can be used to enhance single motor unit action potentials (MUAPs). Traditional spatial filters for surface EMG do not take into consideration that some electrodes could have poor skin contact. In contrast to the traditional a priori defined filters, this study introduces an adaptive spatial filtering method that adapts to the signal characteristics. The adaptive filter, the maximum kurtosis filter (MKF), was obtained by using the linear combination of surrounding channels that maximises kurtosis. The MKF and conventional filters were applied to simulated EMG signals and to real EMG signals recorded with an electrode grid to evaluate their performance in detecting single motor units. The MKF was compared with conventional spatial filtering methods. Simulated signals, with different levels of spatially correlated noise, were used for comparison. The influence of one electrode with poor skin contact was also investigated. The MKF was found to be considerably better at enhancing a single MUAP than conventional methods for all levels of spatial correlation of the noise. For a spatial correlation of 0.97 of the noise, the improvement in the signal-to-noise ratio, where a MUAP could be detected, was at least 6dB. With a simulated poor skin contact for one electrode, the improvement over the other methods was at least 19 dB.