Influence of amplitude cancellation on the accuracy of determining the onset of muscle activity from the surface electromyogram

The purpose of the study was to quantify the influence of amplitude cancellation on the accuracy of detecting the onset of muscle activity based on an analysis of simulated surface electromyographic (EMG) signals. EMG activity of a generic lower limb muscle was simulated during the stance phase of human gait. Surface EMG signals were generated with and without amplitude cancellation by summing simulated motor unit potentials either before (cancellation EMG) or after (no-cancellation EMG) the potentials had been rectified. The two sets of EMG signals were compared at forces of 30% and 80% of maximum voluntary contraction (MVC) and with various low-pass filter cut-off frequencies. Onset time was determined both visually and by an algorithm that identified when the mean amplitude of the signal within a sliding window exceeded a specified standard deviation (SD) above the baseline mean. Onset error was greater for the no-cancellation conditions when determined automatically and by visual inspection. However, the differences in onset error between the two cancellation conditions appear to be clinically insignificant. Therefore, amplitude cancellation does not appear to limit the ability to detect the onset of muscle activity from the surface EMG.     

Application of singular spectrum-based change-point analysis to EMG-onset detection

While many approaches have been proposed to identify the signal onset in EMG recordings, there is no standardized method for performing this task. Here, we propose to use a change-point detection procedure based on singular spectrum analysis to determine the onset of EMG signals. This method is suitable for automated real-time implementation, can be applied directly to the raw signal, and does not require any prior knowledge of the EMG signal’s properties. The algorithm proposed by Moskvina and Zhigljavsky (2003) was applied to EMG segments recorded from wrist and trunk muscles. Wrist EMG data was collected from 9 Parkinson’s disease patients with and without tremor, while trunk EMG data was collected from 13 healthy able-bodied individuals. Along with the change-point detection analysis, two threshold-based onset detection methods were applied, as well as visual estimates of the EMG onset by trained practitioners. In the case of wrist EMG data without tremor, the change-point analysis showed comparable or superior frequency and quality of detection results, as compared to other automatic detection methods. In the case of wrist EMG data with tremor and trunk EMG data, performance suffered because other changes occurring in these signals caused larger changes in the detection statistic than the changes caused by the initial muscle activation, suggesting that additional criteria are needed to identify the onset from the detection statistic other than its magnitude alone. Once this issue is resolved, change-point detection should provide an effective EMG-onset detection method suitable for automated real-time implementation.     

The effect of background muscle activity on computerized detection of sEMG onset and offset

The performance of two computerized algorithms for the detection of muscle onset and offset was compared. The standard deviation (SD) method, a commonly used algorithm, and the approximated generalized likelihood ratio (AGLR) method, a more recently developed algorithm, were evaluated at different levels of background surface EMG (sEMG) activity. For this purpose, the amplitude ratio between the period of muscle inactivity and activity was varied from 0.125 to 1 in artificially assembled sEMG traces. In addition, 1230 real sEMG signals, obtained from various trunk muscles during quick release, were raised to a power of 3 to change their relative amplitude ratio. As the relative level of background activity increased, both the SD and AGLR methods produced longer latencies and detected fewer muscle responses, suggesting that a detection artifact can be introduced if the subject populations being compared have different levels of background muscle activity. Of the two methods, AGLR appears to be the least affected by background activity. However, above the ratio 0.8, results from AGLR are also unreliable, particularly in detecting offsets. Average latency artifacts near this ratio were 8 ms for AGLR and 46 ms for SD.     

Robust Muscle Activity Onset Detection Using an Unsupervised Electromyogram Learning Framework

Accurate muscle activity onset detection is an essential prerequisite for many applications of surface electromyogram (EMG). This study presents an unsupervised EMG learning framework based on a sequential Gaussian mixture model (GMM) to detect muscle activity onsets. The distribution of the logarithmic power of EMG signal was characterized by a two-component GMM in each frequency band, in which the two components respectively correspond to the posterior distribution of EMG burst and non-burst logarithmic powers. The parameter set of the GMM was sequentially estimated based on maximum likelihood, subject to constraints derived from the relationship between EMG burst and non-burst distributions. An optimal threshold for EMG burst/non-burst classification was determined using the GMM at each frequency band, and the final decision was obtained by a voting procedure. The proposed novel framework was applied to simulated and experimental surface EMG signals for muscle activity onset detection. Compared with conventional approaches, it demonstrated robust performance for low and changing signal to noise ratios in a dynamic environment. The framework is applicable for real-time implementation, and does not require the assumption of non EMG burst in the initial stage. Such features facilitate its practical application.     

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.     

表面肌电信号的肌肉疲劳判别研究进展

肌肉在周期的收缩或静态的拉伸过程中,会渐渐进入疲劳状态,肌肉疲劳特性的研究在康复医学、运动医学领域具有重要作用。表面肌电信号是从肌肉表面通过电极记录下来的反映神经肌肉系统活动的一维时间序列非平稳生物电信号,是评价局部肌肉疲劳的有效工具。本研究从时域和频域、时频域线性方法下的测量指标和非线性方法下的指标来综述表面肌电信号的疲劳研究进展,同时比较各种方法的优缺点,并对使用表面肌电信号来判别疲劳研究做了进一步的展望。     

A simulation study to examine the use of cross-correlation as an estimate of surface EMG cross talk.

Cross-correlation between surface electromyogram (EMG) signals is commonly used as a means of quantifying EMG cross talk during voluntary activation. To examine the reliability of this method, the relationship between cross talk and the cross-correlation between surface EMG signals was examined by using model simulation. The simulation results illustrate an increase in cross talk with increasing subcutaneous fat thickness. The results also indicate that the cross-correlation function decays more rapidly with increasing distance from the active fibers than cross talk, which was defined as the normalized EMG amplitude during activation of a single muscle. The influence of common drive and short-term motor unit synchronization on the cross-correlation between surface EMG signals was also examined. While common drive did not alter the maximum value of the cross-correlation function, the correlation increased with increasing motor unit synchronization. It is concluded that cross-correlation analysis is not a suitable means of quantifying cross talk or of distinguishing between cross talk and coactivation during voluntary contraction. Furthermore, it is possible that a high correlation between surface EMG signals may reflect an association between motor unit firing times, for example due to motor unit synchronization.     

Experimental muscle pain increases mechanomyographic signal activity during sub-maximal isometric contractions.

This study was designed to investigate the local effect of experimental muscle pain on the MMG and the surface EMG during a range of sub-maximal isometric contractions. Muscle pain was induced by injections of hypertonic saline into the biceps brachii muscle in 12 subjects. Injections of isotonic saline served as a control. Pain intensity and location, MMG and surface EMG from the biceps brachii were assessed during static isometric (0%, 10%, 30%, 50% and, 70% of the maximal voluntary contraction) and ramp isometric (0-50% of the maximal voluntary contraction) elbow flexions. MMG and surface EMG signals were analyzed in the time and frequency domain. Experimentally induced muscle pain induced an increase in root mean square values of the MMG signal while no changes were observed in the surface EMG. Most likely this increase reflects changes in the mechanical contractile properties of the muscle and indicates compensatory mechanisms, i.e. decreased firing rate and increased twitch force to maintain a constant force output in presence of experimental muscle pain. Under well-controlled conditions, MMG recordings may be more sensitive than surface EMG recordings and clinically useful for detecting non-invasively increased muscle mechanical contributions during muscle pain conditions.     

Comparison of algorithms for estimation of EMG variables during voluntary isometric contractions.

Many algorithms have been described in the literature for estimating amplitude, frequency variables and conduction velocity of the surface EMG signal detected during voluntary contractions. They have been used in different application areas for the non invasive assessment of muscle functions. Although many studies have focused on the comparison of different methods for information extraction from surface EMG signals, they have been carried out under different conditions and a complete comparison is not available. It is the purpose of this paper to briefly review the most frequently used algorithms for EMG variable estimation, compare them using computer generated as well as real signals and outline the advantages and drawbacks of each. In particular the paper focuses on the issue of EMG amplitude estimation with and without pre-whitening of the signal, mean and median frequency estimation with periodogram and autoregressive based algorithms both in stationary and non-stationary conditions, delay estimation for the calculation of muscle fiber conduction velocity.     

Automatic detection of surface EMG activation timing using a wavelet transform based method

The problem of the identification of the muscle contraction timing by using surface electromyographic signal is addressed. The timing detection of the muscular activation in dynamic conditions has a real clinical diagnostic impact. Widely used single threshold methods still rely on the experience of the operator in manually setting that threshold. A new approach to detect the muscular activation intervals, that is based on discontinuities detection in the wavelet domain, is proposed. Accuracy and precision of the algorithm were assessed by using a set of simulated signals obtaining values lower than 11.0 and 8.7 ms for biases and standard deviations of the estimation, respectively. Moreover an experimental application of the algorithm was carried out recruiting a population of 10 able-bodied subjects and processing the myoelectric signals recorded from the lower limb during an isokinetic exercise. The algorithm was able to reveal correctly the timing of muscular activation with performance comparable to the state-of-the-art methods. The detection algorithm is automatic and user-independent, it manages the detection of both onset and offset activation, it can be fruitfully applied even in presence of noise and, therefore, it can be used also by unskilled operators.     

Factors affecting the turns analysis of the interference EMG signal

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

Human Intracranial High Frequency Oscillations (HFOs) Detected by Automatic Time-Frequency Analysis

Objectives

High frequency oscillations (HFOs) have been proposed as a new biomarker for epileptogenic tissue. The exact characteristics of clinically relevant HFOs and their detection are still to be defined.

Methods

We propose a new method for HFO detection, which we have applied to six patient iEEGs. In a first stage, events of interest (EoIs) in the iEEG were defined by thresholds of energy and duration. To recognize HFOs among the EoIs, in a second stage the iEEG was Stockwell-transformed into the time-frequency domain, and the instantaneous power spectrum was parameterized. The parameters were optimized for HFO detection in patient 1 and tested in patients 2–5. Channels were ranked by HFO rate and those with rate above half maximum constituted the HFO area. The seizure onset zone (SOZ) served as gold standard.

Results

The detector distinguished HFOs from artifacts and other EEG activity such as interictal epileptiform spikes. Computation took few minutes. We found HFOs with relevant power at frequencies also below the 80–500 Hz band, which is conventionally associated with HFOs. The HFO area overlapped with the SOZ with good specificity > 90% for five patients and one patient was re-operated. The performance of the detector was compared to two well-known detectors.

Conclusions

Compared to methods detecting energy changes in filtered signals, our second stage - analysis in the time-frequency domain - discards spurious detections caused by artifacts or sharp epileptic activity and improves the detection of HFOs. The fast computation and reasonable accuracy hold promise for the diagnostic value of the detector.     

Timing of cortical excitability changes during the reaction time of movements superimposed on tonic motor activity.

Seated subjects were instructed to react to an auditory cue by simultaneously contracting the tibialis anterior (TA) muscle of each ankle isometrically. Focal transcranial magnetic stimulation of the leg area of the motor cortex (MCx) was used to determine the time course of changes in motor-evoked potential amplitude (MEP) during the reaction time (RT). In one condition the voluntary contraction was superimposed on tonic EMG activity maintained at 10% of maximal voluntary contraction. In the other condition the voluntary contraction was made starting from rest. MEPs in the TA contralateral to the stimulation coil were evoked at various times during the RT in each condition. These were compared to the control MEPs evoked during tonic voluntary activity or with the subject at rest. The RT was measured trial by trial from the EMG activity of the TA ipsilateral to the magnetic stimulus, taking into account the nearly constant time difference between the two sides. The MEPs became far greater than control MEPs during the RT (mean = 332%, SD = 44 %, of control MEPs, P < 0.001) without any measurable change in the background level of EMG activity. The onset of this facilitation occurred on average 12.80 ms (SD = 7.55 ms) before the RT. There was no difference in the onset of facilitation between the two conditions. Because MEPs were facilitated without a change in the background EMG activity, it is concluded that this facilitation is specifically due to an increase of MCx excitability just before voluntary muscle activation. This conclusion is further reinforced by the observation that MEPs evoked by near-threshold anodal stimuli to the MCx were not facilitated during the RT, in contrast to those evoked by near-threshold transcranial magnetic stimulation. However, several observations in the present and previous studies indicate that MEP amplitude may be more sensitive to alpha-motoneuron activity than to motor cortical neuron activity, an idea that has important methodological implications.     

Intramuscular and surface electromyogram changes during muscle fatigue

Twelve male subjects were tested to determine the effects of motor unit (MU) recruitment and firing frequency on the surface electromyogram (EMG) frequency power spectra during sustained maximal voluntary contraction (MVC) and 50% MVC of the biceps brachii muscle. Both the intramuscular MU spikes and surface EMG were recorded simultaneously and analyzed by means of a computer-aided intramuscular spike amplitude-frequency histogram and frequency power spectral analysis, respectively. Results indicated that both mean power frequency (MPF) and amplitude (rmsEMG) of the surface EMG fell significantly (P less than 0.001) together with a progressive reduction in MU spike amplitude and firing frequency during sustained MVC. During 50% MVC there was a significant decline in MPF (P less than 0.001), but this decline was accompanied by a significant increase in rmsEMG (P less than 0.001) and a progressive MU recruitment as evidenced by an increased number of MUs with relatively large spike amplitude. Our data suggest that the surface EMG amplitude could better represent the underlying MU activity during muscle fatigue and the frequency powers spectral shift may or may not reflect changes in MU recruitment and rate-coding patterns.     

Elimination of electrocardiogram contamination from electromyogram signals: An evaluation of currently used removal techniques.

Trunk electromyographic signals (EMG) are often contaminated with heart muscle electrical activity (ECG) due to the proximity of the collection sites to the heart and the volume conduction characteristics of the ECG through the torso. Few studies have quantified ECG removal techniques relative to an uncontaminated EMG signal (gold standard or criterion measure), or made direct comparisons between different methods for a given set of data. Understanding the impacts of both untreated contaminated EMG and ECG elimination techniques on the amplitude and frequency parameters is vital given the widespread use of EMG. The purpose of this study was to evaluate four groups of current and commonly used techniques for the removal of ECG contamination from EMG signals. ECG recordings at two intensity levels (rest and 50% maximum predicted heart rate) were superimposed on 11 uncontaminated biceps brachii EMG signals (rest, 7 isometric and 3 isoinertial levels). The 23 removal methods used were high pass digital filtering (finite impulse response (FIR) using a Hamming window, and fourth-order Butterworth (BW) filter) at five cutoff frequencies (20, 30, 40, 50, and 60 Hz), template techniques (template subtraction and an amplitude gating template), combinations of the subtraction template and high pass digital filtering, and a frequency subtraction/signal reconstruction method. For muscle activation levels between 10% and 25% of maximum voluntary contraction, the template subtraction and BW filter with a 30 Hz cutoff were the two best methods for maximal ECG removal with minimal EMG distortion. The BW filter with a 30 Hz cutoff provided the optimal balance between ease of implementation, time investment, and performance across all contractions and heart rate levels for the EMG levels evaluated in this study.     

A non-invasive method for measuring inspiratory muscle fatigue during progressive isocapnic hyperventilation in man

Eleven normal adults each performed a ten minute progressive isocapnic hyperventilation (PIHV) test in which ventilatory levels were increased every two minutes. All subjects exhibited mechanical fatigue by failing to maintain the target of 80% of maximum voluntary ventilation (MVV). The mean ventilation at this level was 67.5 +/- 1.4% MVV. This fatigue was accompanied by a fall in transdiaphragmatic pressure. During the test the EMG of the sternomastoid (SM) was monitored by surface electrodes and was analyzed using fast-fourier transform. The centroid frequency (Fc) fell as ventilation increased, and correlated negatively with the inability to achieve target ventilation(r = -0.99, p less than 0.015). Five subjects performed the test while the diaphragmatic EMG was recorded from an oesophageal electrode (DIes) and from surface electrodes (DIs). The Fc of DIes fell with increasing ventilation levels (r = -0.95, p less than 0.05) and there was a correlation between the Fc changes of both DIes and the SM (r = -0.92, p less than 0.001). The Fc of DIs did not correlate with either mechanical performance or the Fc of DIes, because of contamination of surface signals by signals from expiratory muscles. It is concluded that the PIHV along with surface monitoring of EMG activity from the sternomastoid can serve as a non-invasive method for evaluating inspiratory muscle fatigue.     

Effect of muscle length on surface EMG wave forms in isometric contractions

To elucidate the influence of muscle length on surface EMG wave form, comparisons were made of surface EMGs of the biceps and triceps brachii muscles during isometric contractions at different muscle lengths. Muscle lengths were altered by setting the elbow joint angle at several intervals between the limits of extension and flexion. The intensity of the isometric contractions was 25% of maximum voluntary contraction at the individual joint angles. Slowing was obvious in the EMG wave forms of biceps as muscle length increased. The so-called 'Piper rhythm' appeared when the muscle was more than moderately lengthened. The slowing trend with muscle lengthening, though less marked, was also seen in triceps. Zero-cross analysis revealed quasi-linear relationships between muscle length and slowing. Frequency analysis confirmed the development of 'Piper rhythm'. An attempt was made to interpret the slowing associated with muscle lengthening in terms of the propagation of myoelectric signals in muscle fibers. given the effect of muscle length on EMG wave forms, a careful control of joint angle may be required in assessing local making fatigue when using EMG spectral indices.     

Muscle activation during maximal voluntary eccentric and concentric knee extension

The aim of this investigation was to study the relationships among movement velocity, torque output and electromyographic (EMG) activity of the knee extensor muscles under eccentric and concentric loading. Fourteen male subjects performed maximal voluntary eccentric and concentric constant-velocity knee extensions at 45, 90, 180 and 360 degrees.s-1. Myoelectric signals were recorded, using surface electrodes, from the vastus medialis, vastus lateralis and rectus femoris muscles. For comparison, torque and full-wave rectified EMG signals were amplitude-averaged through the central half (30 degrees-70 degrees) of the range of motion. For each test velocity, eccentric torque was greater than concentric torque (range of mean differences: 20%-146%, P less than 0.05). In contrast, EMG activity for all muscles was lower under eccentric loading than velocity-matched concentric loading (7%-31%, P less than 0.05). Neither torque output nor EMG activity for the three muscles changed across eccentric test velocities (P greater than 0.05). While concentric torque increased with decreasing velocity, EMG activity for all muscles decreased with decreasing velocity (P less than 0.05). These data suggest that under certain high-tension loading conditions (especially during eccentric muscle actions), the neural drive to the agonist muscles was reduced, despite maximal voluntary effort. This may protect the musculoskeletal system from an injury that could result if the muscle was to become fully activated under these conditions.     

Sleep/wake estimation using only anterior tibialis electromyography data

ABSTRACT: BACKGROUND: In sleep efficiency monitoring system, actigraphy is the simplest and most commonly used device. However, low specificity to wakefulness of actigraphy was revealed in previous studies. In this study, we assumed that sleep/wake estimation using actigraphy and electromyography (EMG) signals would show different patterns. Furthermore, each EMG pattern in two states (sleep, wake during sleep) was analysed. Finally, we proposed two types of method for the estimation of sleep/wake patterns using only EMG signals from anterior tibialis muscles and the results were compared with PSG data. METHODS: Seven healthy subjects and five patients (2 obstructive sleep apnea, 3 periodic limb movement disorder) participated in this study. Night time polysomnography (PSG) recordings were conducted, and electrooculogram, EMG, electroencephalogram, electrocardiogram, and respiration data were collected. Time domain analysis and frequency domain analysis were applied to estimate the sleep/wake patterns. Each method was based on changes in amplitude or spectrum (total power) of anterior tibialis electromyography signals during the transition from the sleep state to the wake state. To obtain the results, leave-one-out-cross-validation technique was adopted. RESULTS: Total sleep time of the each group was about 8 hours. For healthy subjects, the mean epoch-by-epoch results between time domain analysis and PSG data were 99%, 71%, 80% and 0.64 (sensitivity, specificity, accuracy and kappa value), respectively. For frequency domain analysis, the corresponding values were 99%, 73%, 81% and 0.67, respectively. Absolute and relative differences between sleep efficiency index from PSG and our methods were 0.8 and 0.8% (for frequency domain analysis). In patients with sleep-related disorder, our proposed methods revealed the substantial agreement (kappa > 0.61) for OSA patients and moderate or fair agreement for PLMD patients. CONCLUSIONS: The results of our proposed methods were comparable to those of PSG. The time and frequency domain analyses showed the similar sleep/wake estimation performance.     

Heterogeneity of muscle activation in relation to force direction: A multi-channel surface electromyography study on the triceps surae muscle

Several skeletal muscles can be divided into sub-modules, called neuromuscular compartments (NMCs), which are thought to be controlled independently and to have distinct biomechanical functions. We looked for distinct muscle activation patterns in the triceps surae muscle (TS) using surface electromyography (EMG) during voluntary contraction. Nine subjects performed isometric and isotonic plantar flexions combined with forces along pre-defined directions. Besides the forces under the ball of the foot, multi-channel surface EMG was measured with electrodes homogeneously distributed over the entire TS. Using principal component analysis, common (global) components were omitted from the EMG signals, thereby estimating muscle activity sufficiently accurate to track fine fluctuations of force during an isotonic contraction (r = 0.80 ± 0.09). A subsequent cluster analysis showed a topographical organization of co-activated parts of the muscle that was different between subjects. Low and negative correlations between the EMG activity within clusters were found, indicating a substantial heterogeneity of TS activation. The correlations between cluster time series and forces at the foot in specific directions differed substantially between clusters, showing that the differentially activated parts of the TS had specific biomechanical functions.