Analysis and simulation of changes in EMG amplitude during high-level fatiguing contractions

Changes in surface electromyographic (EMG) amplitude during sustained, fatiguing contractions are commonly attributed to variations in muscle fiber conduction velocity (MFCV), motor unit firing rates, transmembrane action potentials and the synchronization or recruitment of motor units. However, the relative contribution of each factor remains unclear. Analytical relationships relating changes in MFCV and mean motor unit firing rates to the root mean square (RMS) and average rectified (AR) value of the surface EMG signal are derived. The relationships are then confirmed using model simulation. The simulations and analysis illustrate the different behaviors of the surface EMG RMS and AR value with changing MFCV and firing rate, as the level of motor unit superposition varies. Levels of firing rate modulation and short-term synchronization that, combined with variations in MFCV, could cause changes in EMG amplitude similar to those observed during sustained isometric contraction of the brachioradialis at 80% of maximum voluntary contraction were estimated. While it is not possible to draw conclusions about changes in neural control without further information about the underlying motor unit activation patterns, the examples presented illustrate how a combined analytical and simulation approach may provide insight into the manner in which different factors affect EMG amplitude during sustained isometric contractions.     

The relationship between the surface EMG and force transients in muscle: Simulation and experimental studies

The surface electromyogrmn (EMG) is an easily measured signal which when quantified by present techniques is a reliable measure of whether a muscle is active, a fairly reliable measure of steady state force and a rather unreliable measure of force transients in muscle. There is a real need for a reliable indicator of dynamic changes in muscle activity for the control of prosthetics, in diagnosis of neuromuscular diseases, in studies of the motor control system and in fundamental studies of muscle mechanics. This paper outlines the principles underlying the development of force and the EMG in muscle. The EMG is a poor estimate of muscle force since it is the result of the linear superposition of biphasic action potentials which result in an interference pattern. This in turn is dependent on the details of the firing intervals for each motor unit, whereas the force is quite insensitive to these details. Experimental results for the human biceps brachii are described in which it was not possible to obtain a consistent estimate of muscle active state from the EMG. An extensive computer simulation was used to explore the relationship between EMG and force under a variety of assumptions. The conclusion is that it is technically impossible to obtain consistent estimates of muscle force (or active state) unless a filter with a time constant of 300 ms is applied to the rectified EMG. This is inconsistent with the estimation of active state for voluntary contractions with observed rise time constants of 30-70 ms. It is susgested that the only solution is to repeat an experiment many times and average the rectified EMG. Unfortunately, in practice it is difficult to repeat certain aspects of voluntary muscular contractions.     

High-precision EMG signal decomposition using communicationtechniques

This paper presents a new approach to the decomposition of electromyographic (EMG) signals. EMG signals consist of a superposition of delayed finite-duration waveforms that carry the information about the firing of different muscle fiber groups. The new approach is based on a communication technical interpretation of the EMG signal. The source is modeled as a signaling system with intersymbol-interference, which encodes a well defined sparse information sequence. This point of view allows a maximum-likelihood (ML) as well as a maximum a posteriori (MAP) estimation of the underlying firing pattern to be made. The high accuracy attainable with the proposed method is illustrated both with measured and artificially generated EMG signals     

一种新型的表面肌电检测与训练系统的软硬件设计

表面肌电检测与训练系统是开发出来用于保健、训练、检测,提高人民生活水平的产品。它具有实用性、便携性,易于操作。表面肌电检测与训练系统由两个主要平台构成：PC计算显示存储平台和下位机采集、显示、传输、存储平台。下位机硬件为ARM7数字处理中心和前端放大滤波模拟处理电路;下位机软件为带触摸屏的嵌入式系统,有USB传输、WiFi无线传输,SD储存等模块,方便高效。上位机实现对肌电信号的非线性动学分析和反馈治疗训练。这个系统具有可行性和灵活性,可用于更深层次的研究。     

On surface EMG spectral characterization and its application to diagnostic classification

Surface EMG was recorded from the biceps with fixed muscle length at S0 percent maximal voluntary contraction. The signal bandpass was 10-230 Hz where most of the surface EMG energy is located. The signal sampled at 500 Hz was found to have a changing spectrum. Stationary segments of 500 ms were subject to linear prediction mathematics to model the system.     

An Application of Signal Processing Techniques to the Study of Myoelectric Signals

This paper describes the use of power spectral density and cumulative power functions in the examination of the electromyogram (EMG). The EMG signals were obtained with surface electrodes from two muscles, the flexor pollicis brevis and the extensor digitorum, in four subjects. Each muscle was studied at two levels of contraction, both before and during fatigue. The power spectral density functions are compared, using a cumulative power difference function and the mean frequency of the spectrum, to determine differences between loading conditions in an individual muscle, before and during fatigue, between different muscles, between individuals (same muscle), and combinations of these conditions.     

Estimation of muscle fiber conduction velocity with a spectral multidip approach

We propose a novel method for estimation of muscle fiber conduction velocity from surface electromyographic (EMG) signals. The method is based on the regression analysis between spatial and temporal frequencies of multiple dips introduced in the EMG power spectrum through the application of a set of spatial filters. This approach leads to a closed analytical expression of conduction velocity as a function of the auto- and cross-spectra of monopolar signals detected along the direction of muscle fibers. The performance of the algorithm was compared with respect to that of the classic single dip approach on simulated and experimental EMG signals. The standard deviation of conduction velocity estimates from simulated single motor unit action potentials was reduced from 1.51 m/s [10 dB signal-to-noise ratio (SNR)] and 1.06 m/s (20 dB SNR) with the single dip approach to 0.51 m/s (10 dB) and 0.23 m/s (20 dB) with the proposed method using 65 dips. When 200 active motor units were simulated in an interference EMG signal, standard deviation of conduction velocity decreased from 0.95 m/s (10 dB SNR) and 0.60 m/s (20 dB SNR) with a single dip to 0.21 m/s (10 dB) and 0.11 m/s (20 dB) with 65 dips. In experimental signals detected from the abductor pollicis brevis muscle, standard deviation of estimation decreased from (mean +/- SD over 5 subjects) 1.25 +/- 0.62 m/s with one dip to 0.10 +/- 0.03 m/s with 100 dips. The proposed method does not imply limitation in resolution of the estimated conduction velocity and does not require any iterative procedure for the estimate since it is based on a closed analytical formulation.     

Circuit for Monitoring the Median Frequency of the Spectrum of the Surface EMG Signal

The power spectrum of the surface EMG signal is known to undergo a compression towards the lower frequencies during sustained muscle contractions. The median frequency appears to be the preferred parameter to monitor this compression. This paper describes a simple circuit which can provide an estimate of the median frequency.     

The EMG-Force Model of Electrically Stimulated Muscle: Dependence on Control Strategy and Predominant Fiber Composition

The EMG-force relationships of electrically stimulated muscle under a wide range of firing rate and recruitment control strategies were studied in a predominantly slow (soleus) and fast (m. gas-trocnemius) twitch muscles with the objective of applying the results in rehabilitative systems where the EMG serves as a force feedback parameter.     

Investigation of optimum electrode locations by using an automatized surface electromyography analysis technique.

Identification of the innervation zone is widely used to optimize the accuracy and precision of noninvasive surface electromyography (EMG) signals because the EMG signal is strongly influenced by innervation zones. However, simply structured fusiform muscle, such as biceps brachii muscle, has been employed mainly due to the simplicity with which the propagation from raw EMG signals can be observed. In this study, the optimum electrode location (OEL), free from innervational influence, was investigated by the propagation pattern of action potentials for brachii muscles and more complicated deltoid muscle structures using an automatized signal analysis technique. The technique employed newly developed computer software with additional clinical uses and minimized subjective differences. EMG signals were recorded using surface array electrodes during voluntary isometric contractions obtained from 12 healthy male subjects. Peaks in EMG signals were detected and averaged for each muscle. The propagation patterns and OEL were examined from biceps brachii muscles for all subjects and from deltoid muscles for seven subjects. The estimated locations were partially confirmed by comparing the root mean squares of the EMG signals. These results show that propagation patterns and OEL could be estimated simply and automatically even from the surface EMG signals of deltoid muscles.     

Motor Unit Firing Rate During Static Contraction Indicated by the Surface EMG Power Spectrum

In this study, power spectral density functions (PSDF's) were computed of interference EMG of various facial and jaw-elevator muscles during nonfatiguing submaximal static contractions, recorded with surface electrodes. A distinct peak was found in the PSDF's in the frequency region below 40 Hz. It was shown that the peak was due to genuine EMG activity and that it could not be considered as an artifact, which was caused by electrode displacements during contraction. An increase of contraction strength resulted in a shift of the peak to higher frequencies and a decrease of peak amplitude relative to the power spectral estimates above 40 Hz, which were shown to be determined by the shape of the motor unit (MU) action potentials. In accordance with mathematical models of the EMG PSDF, it was demonstrated that the peak indicates the dominant firing rate of the sampled MU's. Our results suggest that this can be defined as the firing rate of the first recruited low-threshold MU's, which may be expected to dominate the interference EMG signal because of their preponderance in number. The data further suggest that the peak can be more readily observed in PSDF's of facial and jaw-elevator muscles than in PSDF's of limb muscles. This might be related to differences in MU firing statistics.     

Fatigue compensation of the electromyographic signal for prostheticcontrol and force estimation

During a sustained muscle contraction, the amplitude of electromyographic (EMG) signals increases and the spectrum of the EMG signal shifts toward lower frequencies. These effects are due to muscular fatigue and can cause problems in the control of myoelectric prostheses and in the estimation of contraction level from the EMG signal. It has been well known that the fatigue effects can be explained by the conduction velocity changes during the fatigue process and by the idea that the conduction velocity is linearly proportional to the median frequency of EMG signals. Hence the fatigue process can be monitored by measuring the median frequency. A fatigue compensation preprocessor has been developed. It uses the widely accepted power spectrum density model of EMG signals that contains the conduction velocity as a measure of fatigue. It was verified that the preprocessor scales down the amplitude of the fatigued EMG signal and decompresses the spectrum. Hence, the preprocessor eliminates the increase in amplitude and the shift in frequency and enables consistent EMG signals to be used to control prostheses     

Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential

The electromyographic (EMG) signal provides information about the performance of muscles and nerves. At any instant, the shape of the muscle signal, motor unit action potential (MUAP), is constant unless there is movement of the position of the electrode or biochemical changes in the muscle due to changes in contraction level. The rate of neuron pulses, whose exact times of occurrence are random in nature, is related to the time duration and force of a muscle contraction. The EMG signal can be modeled as the output signal of a filtered impulse process where the neuron firing pulses are assumed to be the input of a system whose transfer function is the motor unit action potential. Representing the neuron pulses as a point process with random times of occurrence, the higher order statistics based system reconstruction algorithm can be applied to the EMG signal to characterize the motor unit action potential. In this paper, we report results from applying a cepstrum of bispectrum based system reconstruction algorithm to real wired-EMG (wEMG) and surface-EMG (sEMG) signals to estimate the appearance of MUAPs in the Rectus Femoris and Vastus Lateralis muscles while the muscles are at rest and in six other contraction positions. It is observed that the appearance of MUAPs estimated from any EMG (wEMG or sEMG) signal clearly shows evidence of motor unit recruitment and crosstalk, if any, due to activity in neighboring muscles. It is also found that the shape of MUAPs remains the same on loading.     

A technique to track individual motor unit action potentials in surface EMG by monitoring their conduction velocities and amplitudes

The speed of propagation of an action potential along a muscle fiber, its conduction velocity (CV), can be used as an indication of the physiological or pathological state of the muscle fiber membrane. The motor unit action potential (MUAP), the waveform resulting from the spatial and temporal summation of the individual muscle fiber action potentials of that motor unit (MU), propagates with a speed referred to as the motor unit conduction velocity (MUCV). This paper introduces a new algorithm, the MU tracking algorithm, which estimates MUCVs and MUAP amplitudes for individual MUs in a localized MU population using SEMG signals. By tracking these values across time, the electrical activity of the localized MU pool can be monitored. An assessment of the performance of the algorithm has been achieved using simulated SEMG signals. It is concluded that this analysis technique enhances the suitability of SEMG for clinical applications and points toward a future of noninvasive diagnosis and assessment of neuromuscular disorders.     

Improved algorithm of continuous abdominal pressure using surface EMG

Conventional rectal catheters, which are used for measurement of abdominal pressure, can cause erroneous results that affect detrusor pressure. An improved method of measuring the continuous abdominal pressure in a non-invasive manner using the surface electromyographic (EMG) signals of the rectus abdominis muscle is devised. This method showed that the surface EMG of the rectus abdominis muscle should be able to be used indirectly for measurement of abdominal pressure in ambulatory urodynamics monitoring studies.     

A multiple-layer finite-element model of the surface EMG signal

The effect of skin, muscle, fat, and bone tissue on simulated surface electromyographic (EMG) signals was examined using a finite-element model. The amplitude and frequency content of the surface potential were observed to increase when the outer layer of a homogeneous muscle model was replaced with highly resistive skin or fat tissue. The rate at which the surface potential decreased as the fiber was moved deeper within the muscle also increased. Similarly, the rate at which the surface potential decayed around the surface of the model, for a constant fiber depth, increased. When layers of subcutaneous fat of increasing thickness were then added to the model, EMG amplitude, frequency content, and the rate of decay of the surface EMG signal around the limb decreased, due to the increased distance between the electrodes and the active fiber. The influence of bone on the surface potential was observed to vary considerably, depending on its location. When located close to the surface of the volume conductor, the surface EMG signal between the bone and the source and directly over the bone increased, accompanied by a slight decrease on the side of the bone distal to the active fiber. The results emphasize the importance of distinguishing between the effects of material properties and the distance between source and electrode when considering the influence of subcutaneous tissue, and suggest possible distortions in the surface EMG signal in regions where a bone is located close to the skin surface.     

Assessment of average muscle fiber conduction velocity from surface EMG signals during fatiguing dynamic contractions

In this paper, we propose techniques of surface electromyographic (EMG) signal detection and processing for the assessment of muscle fiber conduction velocity (CV) during dynamic contractions involving fast movements. The main objectives of the study are: 1) to present multielectrode EMG detection systems specifically designed for dynamic conditions (in particular, for CV estimation); 2) to propose a novel multichannel CV estimation method for application to short EMG signal bursts; and 3) to validate on experimental signals different choices of the processing parameters. Linear adhesive arrays of electrodes are presented for multichannel surface EMG detection during movement. A new multichannel CV estimation algorithm is proposed. The algorithm provides maximum likelihood estimation of CV from a set of surface EMG signals with a window limiting the time interval in which the mean square error (mse) between aligned signals is minimized. The minimization of the windowed mse function is performed in the frequency domain, without limitation in time resolution and with an iterative computationally efficient procedure. The method proposed is applied to signals detected from the vastus laterialis and vastus medialis muscles during cycling at 60 cycles/min. Ten subjects were investigated during a 4-min cycling task. The method provided reliable assessment of muscle fatigue for these subjects during dynamic contractions.     

Fatigue analysis of the surface EMG signal in isometric constant force contractions using the averaged instantaneous frequency

The averaged instantaneous frequency (AIF) is proposed as an alternative method for the frequency analysis of surface electromyography (EMG) in the study of muscle fatigue during sustained, isometric muscle contractions. Results from performance analysis using experimental EMG signals demonstrate the low variability of the proposed frequency variable. Indeed, the AIF measure is shown to perform significantly better than the widely used mean and median frequency variables, in terms of robustness to the length of the analysis window.     

Simulation of surface EMG signals for a multilayer volume conductor with a superficial bone or blood vessel

This study analytically describes surface electromyogram (EMG) signals generated by a planar multilayer volume conductor constituted by different subdomains modeling muscle, bone (or blood vessel), fat, and skin tissues. The bone is cylindrical in shape, with a semicircular section. The flat portion of the boundary of the bone subdomain is interfaced with the fat layer tissue, the remaining part of the boundary is in contact with the muscle layer. The volume conductor is a model of physiological tissues in which the bone is superficial, as in the case of the tibia bone, backbone, and bones of the forearm. The muscle fibers are considered parallel to the axes of the bone, so that the model is space invariant in the direction of propagation of the action potential. The proposed model, being analytical, allows faster simulations of surface EMG with respect to previously developed models including bone or blood vessels based on the finite-element method. Surface EMG signals are studied by simulating a library of single-fiber action potentials (SFAP) of fibers in different locations within the muscle domain, simulating the generation, propagation, and extinction of the action potential. The decay of the amplitude of the SFAPs in the direction transversal to the fibers is assessed. The decay in the direction of the bone has a lower rate with respect to the opposite direction. Similar results are obtained by simulating motor unit action potentials (MUAPs) constituted by 100 fibers with territory 5 mm2. M waves and interference EMG signals are also simulated based on the library of SFAPs. Again, the decay of the amplitude of the simulated interference EMG signals is lower approaching the bone with respect to going farther from it. The findings of this study indicate the effect of a superficial bone in enhancing the EMG signals in the transversal direction with respect to the fibers of the considered muscle. This increases the effect of crosstalk. The same mathematical method used to simulate a superficial bone can be applied to simulate other physiological tissues. For example, superficial blood vessels (e.g., basilic vein, brachial artery) can influence the recorded EMG signals. As the electrical conductivity of blood is high (it is of the same order as the longitudinal conductivity in the muscle), the effect on EMG signals is opposite compared to the effect of a superficial bone.     

Simulation of surface EMG signals generated by muscle tissues with inhomogeneity due to fiber pinnation

Surface electromyographic (EMG) signal modeling has important applications in the interpretation of experimental EMG data. Most models of surface EMG generation considered volume conductors homogeneous in the direction of propagation of the action potentials. However, this may not be the case in practice due to local tissue inhomogeneities or to the fact that there may be groups of muscle fibers with different orientations. This study addresses the issue of analytically describing surface EMG signals generated by bi-pinnate muscles, i.e., muscles which have two groups of fibers with two orientations. The approach will also be adapted to the case of a muscle with fibers inclined in the depth direction. Such muscle anatomies are inhomogeneous in the direction of propagation of the action potentials with the consequence that the system can not be described as space invariant in the direction of source propagation. In these conditions, the potentials detected at the skin surface do not travel without shape changes. This determines numerical issues in the implementation of the model which are addressed in this work. The study provides the solution of the nonhomogenous, anisotropic problem, proposes an implementation of the results in complete surface EMG generation models (including finite-length fibers), and shows representative results of the application of the models proposed.