Changes in EMG activity in the upper trapezius muscle due to local vibration exposure.

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 3 min. 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 (-2.51 Hz) compared to without vibration (-4.04 Hz). 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.

Signal processing of the surface electromyogram to gain insight into neuromuscular physiology.

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).

Test-retest reliability of wavelet - and Fourier based EMG (instantaneous) median frequencies in the evaluation of back and hip muscle fatigue during isometric back extensions.

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.

Firing rate and conduction velocity of single motor units in the trapezius muscle in fibromyalgia patients and healthy controls.

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.

Variations in the axis of motion during head repositioning--a comparison of subjects with whiplash-associated disorders or non-specific neck pain and healthy controls.

BACKGROUND: The ability to reproduce head position can be affected in patients after a neck injury. The repositioning error is commonly used as a measure of proprioception, but variations in the movement might provide additional information. METHODS: The axis of motion and target performance were analyzed during a head repositioning task (flexion, extension and side rotations) for 24 control subjects, 22 subjects with whiplash-associated disorders and 21 with non-specific neck pain. Questionnaires regarding pain intensity and fear avoidance were collected. Head position and axis of motion parameters were calculated using a helical axis model with a moving window of 4 degrees . FINDINGS: During flexion the whiplash group had a larger constant repositioning error than the control group (-1.8(2.9) degrees vs. 0.1(2.4) degrees , P=0.04). The axis was more inferior in both neck pain groups (12.0(1.6)cm vs. 14.5(2.0)cm, P<0.05) indicating movement at a lower level in the spine. Including pain intensity from shoulder and neck region as covariates showed an effect on the axis position (P=0.03 and 0.04). During axial rotation to the left there was more variation in axis direction for neckpain groups as compared with controls (4.0(1.7) degrees and 3.7(2.4) degrees vs. 2.3(1.9) degrees , P=0.01 and 0.05). No significant difference in fear avoidance was found between the two neck pain groups. INTERPRETATION: Measuring variation in the axis of motion together with target performance gives objective measures on proprioceptive ability that are difficult to quantify by visual inspection. Repositioning errors were in general small, suggesting it is not sufficient as a single measurement variable in a clinical situation, but should be measured in combination with other tests, such as range of motion.

Adaptive spatio-temporal filtering of multichannel surface EMG signals.

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.

Inhomogeneities in muscle activation reveal motor unit recruitment.

This paper presents a novel method to quantify spatial changes in muscle activation pattern by multi-channel surface electromyography (MCSEMG) in order to investigate motor unit recruitment variation. The method is based on non-uniform distributions of motor units that cause spatial inhomogeneous muscle activation. To evaluate the method, 15 subjects performed three isometric elbow flexion contractions consisting of slow sinusoidal changes in force ranging from 0% to 80% of the maximal voluntary contraction. MCSEMG electrodes were placed in a 10 x 13 grid over the biceps brachii muscle. From all channels, root mean square (RMS) values of bipolar leadings were computed over 0.5 s epochs over the whole recording. Thereafter, correlation coefficients were calculated between the RMS values at one epoch, with the RMS values at another epoch. Results showed consistent spatial changes in the distribution of RMS at different contraction levels up to 80% of maximal voluntary contraction and when comparing increasing and decreasing contractions at the same force level. These findings are reproducible within and between subjects, and in agreement with physiological phenomena and therefore indicate that the spatial inhomogeneities of motor unit properties in the biceps brachii muscle can be used to study changes in motor unit recruitment.

Improved maximum frequency estimation with application to instantaneous mean frequency estimation of surface electromyography.

The purpose of this study was to improve the maximum-frequency estimation. Three methods to estimate the maximum frequency of a bandlimited signal with additive white noise were compared. Two existing methods, the threshold-crossing method (TCM) and the hybrid method, were modified for time-frequency representations. A novel approach, the running-block threshold method (RBTM), was introduced. Based on calculation of detection probability (sensitivity) the RBTM improved the maximum-frequency estimate as compared with the TCM. The maximum-frequency estimation methods were also used to determine the integration interval for instantaneous mean-frequency (IMNF) estimation from synthesized surface electromyography containing white noise. Results showed that the IMNF estimate was improved by using any of the three methods and that the RBTM gave the best IMNF estimate.

Chronic whiplash associated disorders and neck movement measurements: an instantaneous helical axis approach.

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.

Classification of neck movement patterns related to whiplash-associated disorders using neural networks.

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.