Motor Unit Action Potential Shape Research Articles

Properties of the motor unit action potential shape in proximal and distal muscles of the upper limb in healthy and post-stroke individuals.

Spectral analysis of surface electromyograms (sEMG) is often used to estimate central and peripheral characteristics of a motor unit (MU) population, such as average conduction velocity, proportion of muscle fiber types, and pattern of MU recruitment. This estimation is based on the assumption that the sEMG adequately reflects the frequency characteristics of the underlying MU action potentials (MUAP). However, sEMG has limitations in this respect, based on physiological and non-physiological factors that influence its frequency content. We present a method to examine characteristics of a MU population more reliably by assessing the distributions of frequency content and amplitude for a collection of individual MUAPs, identified using high-density sEMG decomposition. We demonstrate the use of this approach to examine how MU characteristics differ across muscles and in the post-stroke state by presenting preliminary data from deltoid (DELT), biceps (BIC), and finger flexor (FF) MU populations from 12 post-stroke individuals and 8 able-bodied controls. The results show differences in the magnitude and range of MUAP median frequencies across muscles in both groups. The group median values were higher in the stroke group for the DELT and FF and lower in the stroke group for the BIC. The range of frequencies was larger in the stroke group for all muscles. The distribution of MUAP RMS amplitude in both stroke and control groups had a substantially larger range in FF than in DELT and BIC. The group median values for the FF were twice as large in the stroke group. In addition, there were differences in the frequency and amplitude results between MUAP and global sEMG analyses. The implications of these findings and possible applications of the approach are discussed.

Homomorphic Deconvolution for MUAP Estimation From Surface EMG Signals.

This paper presents a technique for parametric model estimation of the motor unit action potential (MUAP) from the surface electromyography (sEMG) signal by using homomorphic deconvolution. The cepstrum-based deconvolution removes the effect of the stochastic impulse train, which originates the sEMG signal, from the power spectrum of sEMG signal itself. In this way, only information on MUAP shape and amplitude were maintained, and then, used to estimate the parameters of a time-domain model of the MUAP itself. In order to validate the effectiveness of this technique, sEMG signals recorded during several biceps curl exercises have been used for MUAP amplitude and time scale estimation. The parameters so extracted as functions of time were used to evaluate muscle fatigue showing a good agreement with previously published results.

Inappropriate interpretation of surface EMG signals and muscle fiber characteristics impedes understanding of the control of neuromuscular function

as the final common pathway from the nervous system to muscle, the motor unit transmits an activation signal generated by the nervous system to engage the contractile proteins and produce the muscle forces needed for reflex responses, automatic behaviors, and voluntary actions ([11][1]). The net

Decomposition of surface EMG signals from cyclic dynamic contractions.

Over the past 3 decades, various algorithms used to decompose the electromyographic (EMG) signal into its constituent motor unit action potentials (MUAPs) have been reported. All are limited to decomposing EMG signals from isometric contraction. In this report, we describe a successful approach to decomposing the surface EMG (sEMG) signal collected from cyclic (repeated concentric and eccentric) dynamic contractions during flexion/extension of the elbow and during gait. The increased signal complexity introduced by the changing shapes of the MUAPs due to relative movement of the electrodes and the lengthening/shortening of muscle fibers was managed by an incremental approach to enhancing our established algorithm for decomposing sEMG signals obtained from isometric contractions. We used machine-learning algorithms and time-varying MUAP shape discrimination to decompose the sEMG signal from an increasingly challenging sequence of pseudostatic and dynamic contractions. The accuracy of the decomposition results was assessed by two verification methods that have been independently evaluated. The firing instances of the motor units had an accuracy of ∼90% with a MUAP train yield as high as 25. Preliminary observations from the performance of motor units during cyclic contractions indicate that during repetitive dynamic contractions, the control of motor units is governed by the same rules as those evidenced during isometric contractions. Modifications in the control properties of motoneuron firings reported by previous studies were not confirmed. Instead, our data demonstrate that the common drive and hierarchical recruitment of motor units are preserved during concentric and eccentric contractions.

Power spectrum of the rectified EMG: influence of motor unit action potential shapes.

The rectification of EMG signals is a preprocessing method widely used for inferring neural connectivity by coherence analysis. The assumption for the use of this non-linear operator is that it enhances the neural information in the signal, i.e. the motor unit spike trains. However, because of non-linearity, it is difficult to predict the effect of rectification on the EMG power spectrum. In this study, we analyze the influence of the motor unit action potential properties on spectral content of the rectified EMG. The results show that changes in the action potential waveforms have a strong influence on the rectified EMG power spectrum, with an effect on estimated coherence functions. Knowledge on the properties of action potentials may be necessary for properly comparing the rectified EMG power spectrum across conditions.

Reliability of spike triggered averaging of the surface electromyogram for motor unit action potential estimation

The reliability of estimated motor unit parameters using spike triggered averaging (STA) of the surface electromyogram (sEMG) has not been tested thoroughly. We investigated factors that may induce amplitude bias in estimated motor unit action potentials (MUAPs) and shape variations. An sEMG record was simulated. MUAPs were then estimated from the STA of the simulated EMG. Variations in MUAP duration led to under-estimation of real MUAP amplitude, while synchronized firing led to over-estimation of amplitude. Spurious firing resulted in over-estimation of the amplitude of small motor units but under-estimation of the amplitude of large ones. Variability in amplitude and high firing rates had minimal influence on amplitude estimation. High firing rates and variation in MUAP duration led to large variations in MUAP shape. Estimation errors also correlated with shape variations. Recommendations to enhance the accuracy of the STA estimates have been proposed.

Assessment of validity of a high-yield surface electromyogram decomposition

BackgroundUsing recordings from a five-pin surface sensor array, a template-based surface electromyogram (sEMG) decomposition system has been developed to identify single motor unit discharge properties. However, the reliability of such template based decomposition results has not been thoroughly examined except by the developers. The focus of this study was to assess the validity of the motor unit decomposition technique, using EMG recordings from the first dorsal interosseous muscle of able-bodied human subjects.MethodsTwo tests were utilized. In the first test, a spike triggered averaging (STA) analysis was used to derive motor unit action potential (MUAP) parameters. We examined these STA derived MUAP shapes after firing times were perturbed by added timing noise. In the second test, a cross-correlation analysis was performed between the sEMG signal and MUAP trains constructed using STA estimates and their firing times.ResultsIn the first test, we found that MUAP shape features deteriorated significantly when rather small (0.6-2 ms) timing errors were added, affirming that the decomposed firing times are presumptively valid. The results of the second test reveal that the cross-correlation index between the EMG and MUAP trains increased monotonically up to 0.71 when the identified MUs were progressively added to reconstructed MUAP trains; however, this increment disappeared when the firing times or the MUAP templates were shifted randomly.ConclusionsBased on our STA selection criteria, our results suggest that the firing times and estimated MUAP shapes for each MU generated by the decomposition algorithms are presumptively valid.

Rectification of the EMG is an unnecessary and inappropriate step in the calculation of Corticomuscular coherence

Corticomuscular coherence (CMC) estimation is a frequency domain method used to detect a linear coupling between rhythmic activity recorded from sensorimotor cortex (EEG or MEG) and the electromyogram (EMG) of active muscles. In motor neuroscience, rectification of the surface EMG is a common pre-processing step prior to calculating CMC, intended to maximize information about action potential timing, whilst suppressing information relating to motor unit action potential (MUAP) shape. Rectification is believed to produce a general shift in the EMG spectrum towards lower frequencies, including those around the mean motor unit discharge rate. However, there are no published data to support the claim that EMG rectification enhances the detection of CMC. Furthermore, performing coherence analysis after the non-linear procedure of rectification, which results in a significant distortion of the EMG spectrum, is considered fundamentally flawed in engineering and digital signal processing.We calculated CMC between sensorimotor cortex EEG and EMG of two hand muscles during a key grip task in 14 healthy subjects. CMC calculated using unrectified and rectified EMG was compared. The use of rectified EMG did not enhance the detection of CMC, nor was there any evidence that MUAP shape information had an adverse effect on the CMC estimation. EMG rectification had inconsistent effects on the power and coherence spectra and obscured the detection of CMC in some cases. We also provide a comprehensive theoretical analysis, which, along with our empirical data, demonstrates that rectification is neither necessary nor appropriate in the calculation of CMC.

Robust decomposition of single-channel intramuscular EMG signals at low force levels

This paper presents a density-based method to automatically decompose single-channel intramuscular electromyogram (EMG) signals into their component motor unit action potential (MUAP) trains. In contrast to most previous decomposition methods, which require pre-setting and (or) tuning of multiple parameters, the proposed method takes advantage of the data-dependent strategies in the pattern recognition procedures. In this method, outliers (superpositions) are excluded prior to classification and MUAP templates are identified by an adaptive density-based clustering procedure. MUAP trains are then identified by a novel density-based classifier that incorporates MUAP shape and discharge time information. MUAP trains are merged by a fuzzy system that incorporates expert human knowledge. Finally, superimpositions are resolved to fill the gaps in the MUAP trains. The proposed decomposition algorithm has been experimentally tested on signals from low-force (≤30% maximal) isometric contractions of the vastus medialis obliquus, vastus lateralis, biceps femoris long-head and tibialis anterior muscles. Comparison with expert manual decomposition that had been verified using a rigorous statistical analysis showed that the algorithm identified 80% of the total 229 motor unit trains with an accuracy greater than 90%. The algorithm is robust and accurate, and therefore it is a promising new tool for decomposing single-channel multi-unit signals.

On the Need for Rectification of Surface EMG

to the editor: The electromyogram (EMG) as an indirect measure of motor unit activity has proved invaluable in motor control studies. Increasingly, the application of frequency domain techniques to EMG signals has provided important insight into the structure and function of the human motor system.

Statistical Technique for Classification of MUAPs for Neuromuscular disease

Electromyograph (EMG) is useful to know the state of a patient under medical diagnosis and treatment. As the number of neuromuscular patients is increasing, it is not possible to take care of all the neuromuscular patients by carrying out manual investigations under all the conditions. Therefore it is required to design a computer aided expert system which can analyze and interpret the EMG signal. The EMG data acquisition and preprocessing, detection of MUAPs, classification of EMGs into similar groups, feature extraction of these groups and their usage in disease classification and diagnostics are the important stages in computer aided EMG analysis and interpretation. The objective of the present work is to detection of MUAPs, classification of EMGs into similar groups for computer aided analysis and interpretation of EMG signals for disease diagnosis. In the work real time recordings of myopathy, motor neuron disease and normal cases have been considered for MUAP segmentation and classification by statistical technique. EMG signal recorded by the needle electrode has been used. 1. INTRODUCTIONbody is composed of various systems and sub-systems. All human body functions are carried out by coordinated and controlled activities of the subsystems. The structural support is provided by bones while the functional support is provided by neuromuscular system. As such, the human body works under the influence of inputs from external environment and from within the body itself. Electromyography is used for the classification of various neuromuscular disorders .The word is made up of three parts: electro- + - myo- + -graphy. -Myo- is from the Greek mys, meaning muscle and graphy comes from the Greek grapho meaning to write. So electromyography literally is the writing (recording) of muscle electricity. The bioelectric potentials associated with the muscle activity constitute the electromyogram .EMG stands for electromyography. It is the study of muscle electrical signals EMGs can be used to detect abnormal muscle electrical activity that can occur in many diseases and conditions. Electromyogram (EMG) is a test that is used to record the electrical activity of muscles. When muscles are active, they produce an electrical current. This current is usually proportional to the level of the muscle activity. The electric current may be produced due to voluntary or involuntary muscle contraction. The EMG activity of voluntary muscle contractions is related to tension. The functional unit of the muscle contraction is a motor unit, which is comprised of a single alpha motor neuron and all the fibers it enervates. This muscle fiber contracts when the action potentials (impulse) of the motor nerve which supplies it reaches a depolarization threshold. The depolarization generates an electromagnetic field and the potential is measured as a voltage. The depolarization, which spreads along the membrane of the muscle, is a muscle action potential. The motor unit action potential is the spatial and temporal summation of the individual muscle action potentials for all the fibers of a single motor unit. Therefore, the EMG signal is the algebraic summation of the motor unit action potentials within the pick-up area of the electrode being used. The electrical signals produced by the muscles and nerves are analyzed to assess the state of neuromuscular functions in subjects with suspected neuromuscular disorders. The repetitive activation of several individual motor units results in superposed pulse train and constitutes the electromyogam signal. The potentials from individual motor units are characterized by unpredictable shapes depending upon the motor unit structure, electrode shapes and sizes, electrode placement, intervening tissues, and the state of neuromusculature. The analysis the EMG is based on its constitute i.e. motor unit action potential (MUAPs). The motor unit is the smallest functional unit of a muscle, which can be activated voluntarily. It consists of a group of muscle fibers, which are innervated from the same motor nerve. The shape of MUAP reflects the pathological and functional states of the motor unit. With increasing muscle force, the EMG signal shows an increase in the number of activated MUAPs recruited at increasing firing rate, making it difficult for the neurophysiologist to distinguish individual MUAP waveforms. In most of the clinical EMG examinations, it is the shape of the action potential that is analyzed for diagnostics. For this purpose, clinical examinations are carried out with the EMG signal recorded at low contraction level of the muscle, usually such that individual motor unit action potentials are distinguishable. This single motor unit activity is the summation of electrical activity of each muscle fiber within a motor unit. Figure1 shows the steps for the preprocessing and classification of MUAP into similar Classes.

A simulation study for a surface EMG sensor that detects distinguishable motor unit action potentials

An advanced volume conductor model was used to simulate the surface-detected motor unit action potentials (MUAPs) due to current sources located at different depths within the muscle tissue of the biceps brachii. Seven different spatial filters were investigated by linear summation of the monopolarly detected surface MUAPs on a square array of nine electrodes. The criterion of the relative energy-of-difference (EOD) between the MUAPs was used to rank spatial filters for their ability to distinguish two motor units located at different depths. Using the same criterion pair wise combinations of spatial filters were ranked for their ability to generate different MUAP shape representations of the same motor unit. In both analyses, the bi-transversal double-differential (BiTDD) configurations and pair wise combinations involving a BiTDD configuration consistently ranked highest. Varying electrode spacing did not change the results in a relevant way. Based on the EOD calculations, a four-channel detection system using all available electrodes of the array is proposed. The implications of using only six electrodes, effectively reducing contact area of the sensor in half, are discussed.

Extraction of motor unit action potentials from electromyographic signals through generative topographic mapping

The extraction of motor unit action potentials (MUAPs) from electromyographic (EMG) signals (also known as EMG decomposition) is an important step in investigations aiming to obtain information on control strategies of the neuromuscular system and its state. For instance, the analysis of the shape of MUAPs and their frequency of occurrence may be used as an additional tool in the detection of some neuromuscular disorders. Although MUAPs can be manually extracted from the EMG, such a procedure is often time consuming and prone to error. In this context, systems which aim to automate the extraction of MUAPs play an important role. First, they allow for the reduction in the processing time of signals, and secondly, they introduce consistency across analyses. In this work, we present an automatic system for the extraction of MUAPs based on generative topographic mapping (GTM), which is a recently developed technique for data clustering and visualization. The system is composed of several signal processing units, including signal filtering and detection, feature selection, data clustering and visualization. Its input is a time-series, representing EMG activity, and its output is the visualization of MUAPs obtained through GTM. The performance of the system was assessed via the analysis of synthetic and experimental EMG signals, detected by means of concentric needle and surface electrodes, collected from healthy subjects executing muscle contractions with distinct levels of force. Our results show that the system is capable of accurately extracting MUAPs from the EMG.

Distinction between computer workers with and without work-related neck–shoulder complaints based on multiple surface EMG parameters

The aim of this study was to investigate whether it is possible to distinguish between computer workers with (cases) and without (controls) work-related neck–shoulder complaints by combining multiple surface electromyography (EMG) parameters in a logistic regression model. Fourteen controls and 13 cases performed five tasks: a unilateral dynamic hand task, a typing task, an editing task, a mouse task and a stress task. EMG of the trapezius muscle was measured using multi-channel electrode arrays. Root-mean-square value and median power frequency, the number of motor unit action potentials (MUAPs) per second and MUAP shape properties were assessed. Logistic regression models were developed for each task with data from ten controls and nine cases. The mouse task resulted in the most discriminative model with correct classification of 89% (jackknife evaluation). MUAP-related parameters were selected as most discriminative. Explorative evaluation with the remaining subjects resulted in a sensitivity and specificity of 3 out of 4 in both groups. In conclusion, a combination of multiple surface EMG parameters was capable of distinguishing computer workers with and without neck–shoulder complaints in a small pilot sample. Relevance to industry The results are promising for development of an objective assessment method that can identify workers who are at risk to develop chronic neck–shoulder complaints.

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.

Motor unit action potential rate and motor unit action potential shape properties in subjects with work-related chronic pain.

The objective of this study was to investigate differences in motor control of the trapezius muscle in cases with work-related chronic pain, compared to healthy controls. Ten cases with chronic pain and 13 controls participated in the study. Electromyographic (EMG) signals were recorded from the upper trapezius during five computer work-related tasks. Motor control was assessed using global root-mean-square value (RMS(G)), motor unit action potential (MUAP) rate (number of MUAPs per second, MR) and two MUAP shape parameters, i.e. root-mean-square (RMS(MUAP)) and median frequency (FMED(MUAP)). MR and FMED(MUAP) were higher for the cases than for the controls (P < 0.05). RMS(MUAP) showed a trend for higher values in the chronic pain group (P < 0.13), whereas RMS(G) did not show a significant difference between the groups. The higher MR, FMED(MUAP) and the trend for higher RMS(MUAP) suggest that more high-threshold MUs contribute to low-level computer work-related tasks in chronic pain cases. Additionally, the results suggest that the input of the central nervous system to the muscle is higher in the cases with chronic pain.

Development of muscle fatigue assessed by using superposition of evoked and volitional myoelectric potentials.

We studied muscle fatigue development using evoked myoelectrical potentials superimposed on volitional ones. The instantaneous frequency of superim posed M-waves and mean power frequencies of volitional electromyography (EMG) declined during sustained contraction, indicating that fatigue progressed. We divided fatigue into 3 phases, with 20 frames in each fatigue phase, corresponding to one-third of the total sample. The instantaneous frequency of superimposed M-waves and mean power frequencies of volitional EMG were correlated during early intensive isometric voluntary contractions and became increasingly uncorrelated as contraction proceeded. The coefficient between the mean power frequency and instantaneous frequency correlation was also greater at the first peak than at the second peak of the superimposed M-wave, indicating that the motor unit action potential was distorted. Distortion in the motor unit action potential shape depends on elongation of the depolarization zone of muscle fiber, because the superimposed M-wave is a peripheral indicator elicited by electrical stimulation. These results suggest that muscle fatigue develops based on the reduction of the conduction velocity of muscle fiber and on the elongation of the depolarization zone of muscle fiber.

A model for the generation of synthetic intramuscular EMG signals to test decomposition algorithms.

As more and more intramuscular electromyogram (EMG) decomposition programs are being developed, there is a growing need for evaluating and comparing their performances. One way to achieve this goal is to generate synthetic EMG signals having known features. Features of interest are: the number of channels acquired (number of detection surfaces), the number of detected motor unit action potential (MUAP) trains, their time-varying firing rates, the degree of shape similarity among MUAPs belonging to the same motor unit (MU) or to different MUs, the degree of MUAP superposition, the MU activation intervals, the amount and type of additive noise. A model is proposed to generate one or more channels of intramuscular EMG starting from a library of real MUAPs represented in a 16-dimensional space using their Associated Hermite expansion. The MUAP shapes, regularity of repetition rate, degree of superposition, activation intervals, etc. may be time variable and are described quantitatively by a number of parameters which define a stochastic process (the model) with known statistical features. The desired amount of noise may be added to the synthetic signal which may then be processed by the decomposition algorithm under test to evaluate its capability of recovering the signal features.

DEVELOPMENT OF MUSCLE FATIGUE ASSESSED BY USING SUPERPOSITION OF EVOKED AND VOLITIONAL MYOELECTRIC POTENTIALS

We studied muscle fatigue development using evoked myoelectrical potentials superimposed on volitional ones. The instantaneous frequency of superimposed M-waves and mean power frequencies of volitional electromyography (EMG) declined during sustained contraction, indicating that fatigue progressed. We divided fatigue into 3 phases, with 20 frames in each fatigue phase, corresponding to one-third of the total sample. The instantaneous frequency of superimposed M-waves and mean power frequencies of volitiona EMG were correlated during early intensive isometric voluntary contractions and became increasingly uncorrelated as contraction proceeded. The coefficient between the mean power frequency and instantaneous frequency correlation was also greater at the first peak than at the second peak of the superimposed M-wave, indicating that the motor unit action potential was distorted. Distortion in the motor unit action potential shape depends on elongation of the depolarization zone of muscle fiber, because the superimposed M-wave is a peripheral indicator elicited by electrical stimulation. These results suggest that muscle fatigue develops based on the reduction of the conduction velocity of muscle fiber and on the elongation of the depolarization zone of muscle fiber.

Computer simulation of post-spike facilitation in spike-triggered averages of rectified EMG.

When the spikes of a motor cortical cell are used to compile a spike-triggered average (STA) of rectified electromyographic (EMG) activity, a post-spike facilitation (PSF) is sometimes seen. This is generally thought to be indicative of direct corticomotoneuronal (CM) connections. However, it has been claimed that a PSF could be caused by synchronization between CM and non-CM cells. This study investigates the generation of PSF using a computer model. A population of cortical cells was simulated, some of which made CM connections to a pool of 103 motoneurons. Motoneurons were simulated using a biophysically realistic model. A subpopulation of the cortical cells was synchronized together. After a motoneuron discharge, a motor unit action potential was generated; these were summed to produce an EMG output. Realistic values were used for the corticospinal and peripheral nerve conduction velocity distribution, for slowing of impulse conduction in CM terminal axons, and for the amount of cortical synchrony. STA of the rectified EMG from all cortical neurons showed PSF; however, these were qualitatively different for CM versus non-CM cells. Using an epoch analysis to determine reliability in a quantitative manner, it was shown that the onset latency of PSF did not distinguish the two classes of cells after 10,000 spikes because of high noise in the averages. The time of the PSF peak and the peak width at half-maximum (PWHM) could separate CM from synchrony effects. However, only PWHM was robust against changes in motor unit action-potential shape and duration and against changes in the width of cortical synchrony. The amplitude of PSF from a CM cell could be doubled by the presence of synchrony. It is proposed that, if a PSF has PWHM < 7 ms, this reliably indicates that the trigger is a CM cell projecting to the muscle whose EMG is averaged. In an analysis of experimental data where macaque motor cortical cells facilitated hand and forearm muscle EMG, 74% of PSFs fulfilled this criterion. The PWHM criterion could be applied to other STA studies in which it is important to exclude the effects of synchrony.