Power line interference rejection from surface electromyography signal using an adaptive algorithm

This work focuses on the power line interference (PLI) rejection from surface EMG signal. It contains three parts: the algorithm, the experimental setting and the results. This study begins with describing the new technique, which consists in filtering respiratory surface electromyogram signals (EMG + PLI), then, becoming familiar with it. The proposed algorithm requires only one channel to both estimating the adaptive filter input reference noise and the EMG signal. The algorithm of PLI rejection has been organized into two steps. The first step insists to apply adaptive filter, especially the LMS one, in which the reference input is mathematically constructed using two different cosine functions; 50 Hz (the fundamental) function and 150 Hz (the first harmonic) function. Whereas, the second step applies the matching pursuit algorithm that uses the cosine packet dictionary to improve the result of PLI obtained at the first step. After trying statistical, as well as mathematical analysis, the complete investigation ensures that all details and steps make proof that our rigorous method is appropriate, we have also compared our method with the previous known techniques.     

The interpretation of abdominal wall muscle recruitment strategies change when the electrocardiogram (ECG) is removed from the electromyogram (EMG)

The purpose of this study was to determine the effect of the ECG artifact on low-level trunk muscle activation amplitudes and assess the effectiveness of two methods used to remove the ECG. Simulations were performed and percent error in root mean square (RMS) amplitudes were calculated from uncontaminated and contaminated EMG signals at various ECG to EMG ratios. Two methods were used to remove the ECG: (1) filtering by adaptive sampling (FAS) and (2) Butterworth high pass filter at 30 Hz (BW-30 Hz HPF). The percent error was also calculated between the ECG removed and the uncontaminated EMG RMS amplitudes. Next, the BW-30 Hz HPF method was used to remove the ECG from 3-bilateral external oblique (EO) muscle sites collected from 30 healthy subjects performing a one handed lift and replace task. Two separate ANOVA models assessed the effects of ECG on the statistical interpretation of EO recruitment strategies. One model included EMG data that contained the ECG and the other model included EMG data after the ECG was removed. Large percent errors were observed when the ECG was not removed. These errors increased with larger ECG to EMG ratios. Both removal methods reduced the errors to below 10%, but the BW-30 Hz HPF method was more time efficient in removing the ECG artifact. Different statistical findings were observed among the muscle sites for the ECG contaminated model compared to the ECG removed model, which resulted in different conclusions concerning neuromuscular control.     

Analysis of phasic and tonic electromyographic signal characteristics: Electromyographic synthesis and comparison of novel morphological and linear-envelope approaches

The pattern of tonic and phasic components in an EMG signal reflects the underlying behaviour of the central nervous system (CNS) in controlling the musculature. One avenue for gaining a better understanding of this behaviour is to seek a quantitative characterisation of these phasic and tonic components. We propose that these signal characteristics can range between unvarying, tonic and intermittent, phasic activation through a continuum of EMG amplitude modulation. In this paper, we present two new algorithms for quantifying amplitude modulation: a linear-envelope approach, and a mathematical morphology approach. In addition we present an algorithm for synthesising EMG signals with known amplitude modulation. The efficacy of the synthesis algorithm is demonstrated using real EMG data. We present an evaluation and comparison of the two algorithms for quantifying amplitude modulation based on synthetic data generated by the proposed synthesis algorithm. The results demonstrate that the EMG synthesis parameters represent 91.9% and 96.2% of the variance of linear-envelopes extracted from lumbo-pelvic muscle EMG signals collected from subjects performing a repetitive-movement task. This depended, however, on the muscle and movement-speed considered (F = 4.02, p < 0.001). Coefficients of determination between input and output amplitude modulation variables were used to quantify the accuracy of the linear-envelope and morphological signal processing algorithms. The linear-envelope algorithm exhibited higher coefficients of determination than the most accurate morphological approach (and hence greater accuracy, T = 8.16, p < 0.001). Similarly, the standard deviation of the coefficients of determination was 1.691 times smaller (p < 0.001). This signal processing algorithm represents a novel tool for the quantification of amplitude modulation in continuous EMG signals and can be used in the study of CNS motor control of the musculature in repetitive-movement tasks.     

Analysis of the tennis racket vibrations during forehand drives: Selection of the mother wavelet

The time–frequency analysis of the tennis racket and hand vibrations is of great interest for discomfort and pathology prevention. This study aimed to (i) to assess the stationarity of the vibratory signal of the racket and hand and (ii) to identify the best mother wavelet to perform future time–frequency analysis, (iii) to determine if the stroke spin, racket characteristics and impact zone can influence the selection of the best mother wavelet. A total of 2364 topspin and flat forehand drives were performed by fourteen male competitive tennis players with six different rackets. One tri-axial and one mono-axial accelerometer were taped on the racket throat and dominant hand respectively. The signal stationarity was tested through the wavelet spectrum test. Eighty-nine mother wavelet were tested to select the best mother wavelet based on continuous and discrete transforms. On average only 25 ± 17%, 2 ± 5%, 5 ± 7% and 27 ± 27% of the signal tested respected the hypothesis of stationarity for the three axes of the racket and the hand respectively. Regarding the two methods for the detection of the best mother wavelet, the Daubechy 45 wavelet presented the highest average ranking. No effect of the stroke spin, racket characteristics and impact zone was observed for the selection of the best mother wavelet. It was concluded that alternative approach to Fast Fourier Transform should be used to interpret tennis vibration signals. In the case where wavelet transform is chosen, the Daubechy 45 mother wavelet appeared to be the most suitable.     

A real time electromyostimulator linked with EMG analysis device

In this study, a new system composed of two modules (electromyostimulation + electromyography recording) is presented. It can analyze in real time EMG signals during electromyostimulation. In addition, we propose a new method based on wavelet decomposition to analyze changes in M-wave characteristics. It leads to introduce a new index related to muscular fatigue.     

Removal of ECG interference from surface respiratory electromyography

The signal to noise ratio (SNR) of surface respiratory electromyography signal is very low. Indeed EMG signal is contaminated by different types of noise especially the cardiac artefact ECG. This article explores the problem of removing ECG artefact from respiratory EMG signal. The new method uses an adaptive structure with an electrocardyographic ECG reference signal carried out by wavelet decomposition. The proposed algorithm requires only one channel to both estimating the adaptive filter input reference noise and the respiratory EMG signal. This new technique demonstrates how two steps will be combined: the first step decomposes the signal with forward discrete wavelet transform into sub-bands to get the wavelet coefficients. Then, an improved soft thresholding function was applied. And the ECG input reference signal is reconstructed with the transformed coefficients whereas, the second uses an adaptive filter especially the LMS one to remove the ECG signal. After trying statistical as well as mathematical analysis, the complete investigation ensures that all details and steps make proof that our rigorous method is appropriate. Compared to the results obtained using previous techniques, the results achieved using the new algorithm show a significant improvement in the efficiency of the ECG rejection.     

Electrode placement over the innervation zone affects the low-, not the high-frequency portion of the EMG frequency spectrum

The purpose of this study was to use a wavelet-based signal processing technique to examine the influence of electrode placement over the innervation zone (IZ) on the shape of the electromyographic (EMG) frequency spectrum. Ten healthy males (mean ± SD age = 23.6 ± 3.0 years) performed isometric muscle actions of the dominant leg extensors at 10%, 40%, 70%, and 100% of the maximum voluntary contraction (MVC). Surface EMG signals were detected simultaneously from the vastus lateralis with two bipolar electrode arrangements. One of the electrode arrangements had its center point located directly over the IZ, while the other arrangement had its center point distal to the IZ (i.e., 20 mm away). All EMG signals were processed with a wavelet-based procedure. The results showed that for all isometric torque levels, the EMG signals from the distal electrode arrangement demonstrated greater total intensity values than those for the IZ arrangement for frequencies ranging from approximately 2 to 110 Hz. There were no consistent differences, however, between the IZ and distal electrode arrangements for total EMG intensity values above 110 Hz. Thus, these findings indicated that electrode placement over the IZ affected primarily the low-, rather than the high-frequency portion of the EMG frequency spectrum.     

Establishment of a recording method for surface electromyography in the iliopsoas muscle

We examined the availability and reliability of surface electromyography (EMG) signals from the iliopsoas muscle (IL). Using serial magnetic resonance images from fifty healthy young males, we evaluated whether the superficial region of IL was adequate for attaching surface EMG electrodes. Subsequently, we assessed EMG cross-talk from the sartorius muscle (SA)—the nearest to IL—using a selective cooling method in fourteen subjects. The skin above SA was cooled, and the median frequencies of EMG signals from IL and SA were determined. The maximum voluntary contraction during isometric hip flexion was measured before and after selective cooling, and surface EMG signals from SA and IL were measured. The superficial area of IL was adequately large (13.2 ± 2.7 cm²) for recording surface EMG in all fifty subjects. The maximum perimeter for the medial–lateral skin facing IL was noted at a level 3–5 cm distal to the anterior superior iliac spine. Following cooling, the median frequency for SA decreased significantly (from 70.1 to 51.9 Hz, p < 0.001); however, that for IL did not alter significantly. These results demonstrated that EMG cross-talk from SA was negligible for surface EMG signals from IL during hip flexion.     

EMG normalization to study muscle activation in cycling

The value of electromyography (EMG) is sensitive to many physiological and non-physiological factors. The purpose of the present study was to determine if the torque–velocity test (T–V) can be used to normalize EMG signals into a framework of biological significance. Peak EMG amplitude of gluteus maximus (GMAX), vastus lateralis (VL), rectus femoris (RF), biceps femoris long head (BF), gastrocnemius medialis (GAS) and soleus (SOL) was calculated for nine subjects during isometric maximal voluntary contractions (IMVC) and torque–velocity bicycling tests (T–V). Then, the reference EMG signals obtained from IMVC and T–V bicycling tests were used to normalize the amplitude of the EMG signals collected for 15 different submaximal pedaling conditions. The results of this study showed that the repeatability of the measurements between IMVC (from 10% to 23%) and T–V (from 8% to 20%) was comparable. The amplitude of the peak EMG of VL was 99 ± 43% higher (p < 0.001) when measured during T–V. Moreover, the inter-individual variability of the EMG patterns calculated for submaximal cycling exercises differed significantly when using T–V bicycling normalization method (GMAX: 0.33 ± 0.16 vs. 1.09 ± 0.04, VL: 0.07 ± 0.02 vs. 0.64 ± 0.14, SOL: 0.07 ± 0.03 vs. 1.00 ± 0.07, RF: 1.21 ± 0.20 vs. 0.92 ± 0.13, BF: 1.47 ± 0.47 vs. 0.84 ± 0.11). It was concluded that T–V bicycling test offers the advantage to be less time and energy-consuming and to be as repeatable as IMVC tests to measure peak EMG amplitude. Furthermore, this normalization method avoids the impact of non-physiological factors on the amplitude of the EMG signals so that it allows quantifying better the activation level of lower limb muscles and the variability of the EMG patterns during submaximal bicycling exercises.     

Variance-based signal conditioning technique: Comparison to a wavelet-based technique to improve spike detection in multiunit intrafascicular recordings

Detection of single unit action potentials (APs) from peripheral nerve recordings is complicated by low signal-to-noise ratio (SNR) due to the activity of nearby muscles, interference from more distant nerve fibers, and thermal noise from the neural interface. In this study, we propose a novel signal conditioning technique for multiunit signals (i.e. a signal comprised of multiple units coming from different nerve fibers), based on the variance to be applied prior to detection of APs. The proposed technique was tested on experimental and simulated intrafascicular recordings; and was compared to a wavelet-based conditioning (also applied before AP detection). The outputs of both conditioning schemes were sent to an AP detection algorithm that used a simple threshold (equal to the standard deviation of the signal). The overall performance of the detection phase was superior when using the wavelet-based conditioned signal especially for SNR ≤ 2 dB. However, when using the variance-based conditioned signal, the AP detection phase resulted in lower number of false positives for SNR > 2 dB. The novel variance-based method improves the SNR by attenuating the background noise between APs and can be applied as pre-conditioning processing for AP detection.     

Use of supervised discretization with PCA in wavelet packet transformation-based surface electromyogram classification

This paper describes a preprocessing stage for nonlinear classifier used in wavelet packet transformation (WPT)-based multichannel surface electromyogram (EMG) classification. The preprocessing stage named sdPCA, which consists of supervised discretization coupled with principal component analysis (PCA), was developed for improving surface EMG classifier generalization ability and training speed on overlap segmented signals. The sdPCA outperforms the fast correlation-based filter (FCBF), PCA, supervised discretization, and their combinations in terms of the highest generalization ability, fast training speed, the small feature size, and an ability to reduce the risks of developing oscillation and being trapped in nonlinear classifier training. The experiments were conducted on a data set consisting of 4-channel surface EMG signals measured from 6 hand and wrist gestures of 12 subjects. The experimental results indicate that the classification system using sdPCA has the highest generalization ability along with the second fastest training speed. The classification accuracy in 12 subjects of the system using sdPCA is 93.30 ± 2.42% taking 400 epochs for training by overlap segmented signals within 100 s. This result is very attractive for further development because we can achieve high-classification accuracy for large data sets by means of the proposed sdPCA without the application of additional algorithms such as local discriminant bases (LDB), majority voting (MV), or WPT sub-bands clustering.     

The highest antagonistic coactivation of the vastus intermedius muscle among quadriceps femoris muscles during isometric knee flexion

Although the possibility that the vastus intermedius (VI) muscle contributes to flexion of the knee joint has been suggested previously, the detail of its functional role in knee flexion is not well understood. The purpose of this study was to examine the antagonist coactivation of VI during isometric knee flexion. Thirteen men performed 25–100% of maximal voluntary contraction (MVC) at 90°, 120°, and 150° knee joint angles. Surface electromyography (EMG) of the four individual muscles in the quadriceps femoris (QF) was recorded and normalized by the EMG signals during isometric knee extension at MVC. Cross-talk on VI EMG signal was assessed based on the median frequency response to selective cooling of hamstring muscles. Normalized EMG of the VI was significantly higher than that of the other synergistic QF muscles at each knee joint angle (all P < 0.05) with minimum cross-talk from the hamstrings to VI. There were significant correlations between the EMG signal of the hamstrings and VI (r = 0.55–0.85, P < 0.001). These results suggest that VI acts as a primary antagonistic muscle of QF during knee flexion, and that VI is presumably a main contributor to knee joint stabilization.     

Knee angle-specific MVIC for triceps surae EMG signal normalization in weight and non weight-bearing conditions

Varying the degree of weight-bearing (WB) and/or knee flexion (KF) angle during a plantar-flexion maximal voluntary isometric contraction (MVIC) has been proposed to alter soleus and/or gastrocnemius medialis and lateralis activation. This study compared the surface EMG signals from the triceps surae of 27 men and 27 women during WB and non weight bearing (NWB) plantar-flexion MVICs performed at 0° and 45° of KF. The aim was to determine which condition was most effective at eliciting the greatest EMG signals from soleus, gastrocnemius medialis, and gastrocnemius lateralis, respectively, for subsequent use for the normalization of EMG signals. WB was more effective than NWB at eliciting the greatest signals from soleus (p = 0.0021), but there was no difference with respect to gastrocnemius medialis and lateralis (p ? 0.2482). Although the greatest EMG signals during MVICs were more frequently elicited at 0° of KF from gastrocnemius medialis and lateralis, and at 45° from soleus (p < 0.001); neither angle consistently captured peak gastrocnemius medialis, gastrocnemius lateralis or soleus activity. The present findings encourage more consistent use of WB plantar flexion MVICs for soleus normalization; confirm that both WB and NWB procedures can elicit peak gastrocnemius activity; and emphasize the fact that no single KF angle consistently evokes selective maximal activity of any individual triceps surae muscle.     

Adaptations of upper trapezius muscle activity during sustained contractions in women with fibromyalgia

The study compared the distribution of electromyographic (EMG) signal amplitude in the upper trapezius muscle in 10 women with fibromyalgia and in 10 healthy women before and after experimentally-induced muscle pain. Surface EMG signals were recorded over the right upper trapezius muscle with a 10 × 5 grid of electrodes during 90° shoulder abduction sustained for 60 s. The control subjects repeated the abduction task following injections of isotonic and hypertonic (painful) saline into the upper trapezius muscle. The EMG amplitude was computed for each electrode pair and provided a topographical map of the distribution of muscle activity. The pain level rated by the patients at the beginning of the sustained contraction was 5.9 ± 1.5. The peak pain intensity for the control group following the injection of hypertonic saline was 6.0 ± 1.6. During the sustained contractions, the EMG amplitude increased relatively more in the cranial than caudal region of the upper trapezius muscle for the control subjects (shift in the distribution of EMG amplitude: 2.3 ± 1.3 mm; P < 0.01). The patient group showed lower average EMG amplitude than the controls during the contraction (P < 0.05) and did not show different changes in EMG amplitude between different regions of the upper trapezius. A similar behavior was observed for the control group following injection of hypertonic saline. The results indicate that muscle pain prevents the adaptation of upper trapezius activity during sustained contractions as observed in non-painful conditions, which may induce overuse of similar muscle compartments with fatigue.     

Myoelectric manifestations of fatigue in vastus lateralis,medialis obliquus and medialis longus muscles

The purpose of this study was to determine whether surface electromyography (EMG) assessment of myoelectric manifestations of muscle fatigue is capable of detecting differences between the vastus lateralis and medialis muscles which are consistent with the results of previous biopsy studies. Surface EMG signals were recorded from the vastus medialis longus (VML), vastus medialis obliquus (VMO) and vastus lateralis (VL) muscles during isometric knee extension contractions at 60% and 80% of the maximum voluntary contraction (MVC) for 10 s and 60 s, respectively. Initial values and rate of change of mean frequency (MNF), average rectified value (ARV) and conduction velocity (CV) of the EMG signal were calculated. Comparisons between the two force levels revealed that the initial values of MNF for the VL muscle were greater at 80% MVC compared to 60% MVC (P < 0.01). Comparisons between the vasti muscles demonstrated lower initial values of CV for VMO compared to VL at 60% MVC (P < 0.01) and lower than VML and VL at 80% MVC (P < 0.01). In addition, initial values of MNF were higher for VL with respect to both VML and VMO at 80% MVC (P < 0.01) and initial estimates of ARV were higher for VMO compared to VML at both force levels (P < 0.01 at 60% MVC and P < 0.05 at 80% MVC). For the sustained contraction at 80% MVC, VL demonstrated a greater decrease in CV over time compared to VMO (P < 0.05).These findings suggest that surface EMG signals and their time course during sustained isometric contractions may be useful to non-invasively describe functional differences between the vasti muscles.     

Comparison of the muscle activation pattern for the vastus lateralis before and after an 8-week resistance training program

The purpose of this investigation was to use wavelet analyses and pattern classification techniques to examine potential changes in the joint time–frequency distribution of surface electromyographic (EMG) signals due to an 8-week resistance training program. Thirteen untrained men (mean ± S.D. age = 22.2 ± 4.0 yrs) performed eight separate submaximal isometric muscle actions of the dominant leg extensors in 10% increments from 10 to 80% of the maximum voluntary contraction (MVC). During each muscle action, a monopolar surface EMG signal was detected from the vastus lateralis. All signals were then analyzed with a wavelet analysis designed specifically for EMG signals, and the resulting intensity patterns were classified using pattern classification techniques into their respective pre-training versus post-training categories. The results showed accuracy rates (% of correctly classified patterns) that ranged from approximately 62 to 92%, but these rates did not change consistently with increases in force. In addition, for five of the eight submaximal force levels, the classification was considered to be significantly different from random. Thus, although the differences between the pre- and post-training EMG intensity patterns were not always consistent, our findings did suggest that there is the potential for wavelet analyses and pattern classification techniques to be used to examine neuromuscular adaptations during resistance training.     

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

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

Cross-talk from adjacent muscle has a negligible effect on surface electromyographic activity of vastus intermedius muscle during isometric contraction

The purposes of this study were to attempt to record surface electromyography (EMG) from the superficial region of vastus intermedius (VI) and to investigate the influence of adjacent muscle activity on surface EMG of VI. First, serial axial magnetic resonance imaging of the thigh was performed for 45 healthy young men to determine morphological characteristics of the VI. Second, surface EMG activity of the VI and other quadriceps femoris (QF) muscle group components were recorded in maximum voluntary contraction during isometric knee extension from 11 healthy young men. To test cross-talk of EMG signals between VI and the nearest adjacent muscle, vastus lateralis (VL), we applied cooling for 20-min on VL to selectively alter activity. Cooling the skin above a muscle is known to decrease median frequency (MF) of EMG signal of the muscle. All subjects displayed a superficial region in VI sufficiently large (14 cm²) to record surface EMG. Surface EMG of VI could be detected in the same scale as other QF muscle group components. Cooling induced a significant MF decrease only in VL (from 92.5 to 44.2 Hz, p < 0.001), but no significant change was observed in VI (from 63.8 to 61.7 Hz). From this result, we concluded the muscle activity of VL is negligible on surface EMG detected from VI during isometric contraction.     

Parkinson’s disease and sex-related differences in electromyography during daily life

Scope: Daily bilateral electromyography (EMG) recordings reveal muscle activation patterns implicated in asymmetric Parkinson’s disease (PD)-related functional decline. Also, daily EMG recordings reveal sex-differences in muscle activity that give rise to unique PD presentation in males and females. Purpose: Quantify handgrip strength and daily muscle quiescence through analysis of gaps in the EMG signal in males and females with PD. Bilateral daily EMG was recorded and normalized to maximal voluntary exertions (MVE). EMG gap was defined as <1% amplitude of MVE for >0.1 s and characterized as number, duration and time occupied by gaps. A dynamometer evaluated maximal grip-strength. Three-way repeated measures ANOVA examined differences in gap characteristics and strength. Gap duration was shorter (p = 0.04) and occupied less time (p = 0.02) in PD than controls. Females had fewer gaps with shorter duration (p = 0.004), occupying less time (p = 0.004) compared with males. Gaps were fewer (p = 0.04) and occupied less time (p = 0.01) on more-affected than less-affected side. PD was weaker than controls (p = 0.04), females were weaker than males (p = 0.00), and the more-affected PD side was weaker than less-affected (p = 0.04). Conclusions: Quantification of muscle quiescence through gaps in the EMG signal recorded during daily life provides insight into mechanisms underlying differential change in functional performance in males and females with PD.