Motor Unit Action Potential Waveforms Research Articles

Measuring the accuracies of motor unit firing times and action potential waveforms derived from surface electromyographic decomposition

This study included spike trigger averaging (STA) procedures to examine the acceptability of the Precision Decomposition (PD) III derived motor unit action potential (MUAP) trains that met the >90% accuracy criteria from the reconstruct-and-test. MUs met the >90% accuracy criteria from the reconstruct-and-test with STA procedures then applied. Y-intercepts and slopes were calculated for the firing rate- and MUAP amplitude-recruitment threshold relationships. Gaussian noise (1% of the SD of the mean interspike interval) was added to the firing times with the changes in MUAPs quantified. A total of 455 MUs were decomposed with 155 MUs removed as a result of the reconstruct-and-test. Five additional MUs were excluded via the STA criteria. The MUAP waveforms deteriorated with the inclusion of Gaussian noise. There were differences in the derived action potentials amplitudes of higher-threshold MUs between the PD III algorithm and the STA procedure. There was excellent agreement among the slopes and y-intercepts between the relationships that included or excluded MUs that did not meet the STA criteria. There was good agreement between the MUAP amplitude-recruitment threshold relationships derived from the PD III and STA procedure. The addition of the STA procedures did not alter the MU-derived relationships.

Properties of Motor Units of Elbow and Ankle Muscles Decomposed Using High-Density Surface EMG.

Analyses of motor unit activity provide a window to the neural control of motor output. In recent years, considerable advancements in surface EMG decomposition methods have allowed for the discrimination of dozens of individual motor units across a range of muscle forces. While these non-invasive methods show great potential as an emerging technology, they have difficulty discriminating a representative sample of the motor pool. In the present study, we investigate the distribution of recruitment thresholds and motor unit action potential waveforms obtained from high density EMG across four muscles: soleus, tibialis anterior, biceps brachii, and triceps brachii. Ten young and healthy control subjects generated isometric torque ramps between 10-50% maximum voluntary torque during elbow or ankle flexion and extension. Hundreds of motor unit spike trains were decomposed for each muscle across all trials. For lower contraction levels and speeds, surface EMG decomposition discriminated a large number of low-threshold units. However, during contractions of greater speed and torque level the proportion of low threshold motor units decomposed was reduced, resulting in a relatively uniform distribution of recruitment thresholds. The number of motor units decomposed decreased as the contraction level and speed increased. The decomposed units showed a wide range of recruitment thresholds and motor unit action potential amplitudes. In conclusion, although surface EMG decomposition is a useful tool to study large populations of motor units, results of such methods should be interpreted in the context of limitations in sampling of the motor pool.

Recursive Decomposition of Electromyographic Signals With a Varying Number of Active Sources: Bayesian Modeling and Filtering.

This paper describes a sequential decomposition algorithm for single-channel intramuscular electromyography (iEMG) generated by a varying number of active motor neurons. As in previous work, we establish a hidden Markov model of iEMG, in which each motor neuron spike train is modeled as a renewal process with inter-spike intervals following a discrete Weibull law and motor unit action potentials are modeled as impulse responses of linear time-invariant systems with known prior. We then expand this model by introducing an activation vector associated with the state vector of the hidden Markov model. This activation vector represents recruitment/derecruitment of motor units and is estimated together with the state vector using Bayesian filtering. Non-stationarity of the model parameters is addressed by means of a sliding window approach, thus making the algorithm adaptive to variations in contraction force and motor unit action potential waveforms. The algorithm was validated using simulated and experimental iEMG signals with varying number of active motor units. The experimental signals were acquired from the tibialis anterior and abductor digiti minimi muscles by fine wire and needle electrodes. The decomposition accuracy in both simulated and experimental signals exceeded 90%. The recruitment/derecruitment was successfully tracked by the algorithm. Because of its parallel structure, this algorithm can be efficiently accelerated, which lays the basis for its real-time applications in human-machine interfaces. The proposed method substantially broadens the domains of applicability of the algorithm.

Coherence of the Surface EMG and Common Synaptic Input to Motor Neurons.

Coherence between electromyographic (EMG) signals is often used to infer the common synaptic input to populations of motor neurons. This analysis, however, may be limited due to the filtering effect of the motor unit action potential waveforms. This study investigated the ability of surface EMG–EMG coherence to predict common synaptic input to motor neurons. Surface and intramuscular EMG were recorded from two locations of the tibialis anterior muscle during steady ankle dorsiflexions at 5 and 10% of the maximal force in 10 healthy individuals. The intramuscular EMG signals were decomposed to identify single motor unit spike trains. For each trial, the strength of the common input in different frequency bands was estimated from the coherence between two cumulative spike trains, generated from sets of single motor unit spike trains (reference measure). These coherence values were compared with those obtained from the coherence between the surface EMG signals (raw, rectified, and high-passed filtered at 250 Hz before rectification) using linear regression. Overall, the high-pass filtering of the EMG prior to rectification did not substantially change the results with respect to rectification only. For both signals, the correlation of EMG coherence with motor unit coherence was strong at 5% MVC (r2 > 0.8; p < 0.01), but only for frequencies > 5 Hz. At 10% MVC, the correlation between EMG and motor unit coherence was only significant for frequencies > 15 Hz (r2 > 0.8; p < 0.01). However, when using raw EMG for coherence analysis, the only significant relation with motor unit coherence was observed for the bandwidth 5–15 Hz (r2 > 0.68; p = 0.04). In all cases, there was no association between motor unit and EMG coherence for frequencies < 5 Hz (r2 ≤ 0.2; p ≥ 0.51). In addition, a substantial error in the best linear fit between motor unit and EMG coherence was always present. In conclusion, high-frequency (>5 Hz) common synaptic inputs to motor neurons can partly be estimated from the rectified surface EMG at low-level steady contractions. The results, however, suggest that this association is weakened with increasing contraction intensity and that input at lower frequencies during steady isometric contractions cannot be detected accurately by surface EMG coherence.

Sliding window averaging in normal and pathological motor unit action potential trains

ObjectiveTo evaluate the performance of a recently proposed motor unit action potential (MUAP) averaging method based on a sliding window, and compare it with relevant published methods in normal and pathological muscles. MethodsThree versions of the method (with different window lengths) were compared to three relevant published methods in terms of signal analysis-based merit figures and MUAP waveform parameters used in the clinical practice. 218 MUAP trains recorded from normal, myopathic, subacute neurogenic and chronic neurogenic muscles were analysed. Percentage scores of the cases in which the methods obtained the best performance or a performance not significantly worse than the best were computed. ResultsFor signal processing figures of merit, the three versions of the new method performed better (with scores of 100, 86.6 and 66.7%) than the other three methods (66.7, 25 and 0%, respectively). In terms of MUAP waveform parameters, the new method also performed better (100, 95.8 and 91.7%) than the other methods (83.3, 37.5 and 25%). ConclusionsFor the types of normal and pathological muscle studied, the sliding window approach extracted more accurate and reliable MUAP curves than other existing methods. SignificanceThe new method can be of service in quantitative EMG.

Intramuscular EMG Decomposition Basing on Motor Unit Action Potentials Detection and Superposition Resolution.

A novel electromyography (EMG) signal decomposition framework is presented for the thorough and precise analysis of intramuscular EMG signals. This framework first detects all of the active motor unit action potentials (MUAPs) and assigns single MUAP segments to their corresponding motor units. MUAP waveforms that are found to be superimposed are then resolved into their constituent single MUAPs using a peel-off approach and similarly assigned. The method is composed of six stages of analytical procedures: preprocessing, segmentation, alignment and feature extraction, clustering and refinement, supervised classification, and superimposed waveform resolution. The performance of the proposed decomposition framework was evaluated using both synthetic EMG signals and real recordings obtained from healthy and stroke participants. The overall detection rate of MUAPs was 100% for both synthetic and real signals. The average accuracy for synthetic EMG signals was 87.23%. Average assignment accuracies of 88.63 and 94.45% were achieved for the real EMG signals obtained from healthy and stroke participants, respectively. Results demonstrated the ability of the developed framework to decompose intramuscular EMG signals with improved accuracy and efficiency, which we believe will greatly benefit the clinical utility of EMG for the diagnosis and rehabilitation of motor impairments in stroke patients.

Sliding window averaging for the extraction of representative waveforms from motor unit action potential trains

Abstract In quantitative electromyography (EMG), the set of potentials that constitute a motor unit action potential (MUAP) train are represented by a single waveform from which various parameters are determined in order to characterize the MUAP for diagnostic analysis. Several methods that extract such a waveform are currently available, and they are, in essence, based on two operations: averaging and selection, which are performed either sample-by-sample or on the whole-potential. We present a new approach that carries out selection and averaging on a local interval basis. We tested our algorithm with a dataset of MUAP records extracted from the tibialis anterioris muscle of healthy subjects and compared it with some of the most relevant state-of-the-art methods considered in a previous work (Malanda et al., J. Electromyogr. Kinesiol., 2015). The comparison covered general purpose signal processing figures of merit and clinically used MUAP waveform parameters. Significantly better results in both sets of figures of merit were obtained with the new approach. In addition, relative to the other algorithms tested, the new approach required fewer potentials from the MUAP set to obtain an accurate representative waveform.

Averaging methods for extracting representative waveforms from motor unit action potential trains

In the context of quantitative electromyography (EMG), it is of major interest to obtain a waveform that faithfully represents the set of potentials that constitute a motor unit action potential (MUAP) train. From this waveform, various parameters can be determined in order to characterize the MUAP for diagnostic analysis.The aim of this work was to conduct a thorough, in-depth review, evaluation and comparison of state-of-the-art methods for composing waveforms representative of MUAP trains. We evaluated nine averaging methods: Ensemble (EA), Median (MA), Weighted (WA), Five-closest (FCA), MultiMUP (MMA), Split-sweep median (SSMA), Sorted (SA), Trimmed (TA) and Robust (RA) in terms of three general-purpose signal processing figures of merit (SPMF) and seven clinically-used MUAP waveform parameters (MWP). The convergence rate of the methods was assessed as the number of potentials per MUAP train (NPM) required to reach a level of performance that was not significantly improved by increasing this number. Test material comprised 78 MUAP trains obtained from the tibialis anterioris of seven healthy subjects.Error measurements related to all SPMF and MWP parameters except MUAP amplitude descended asymptotically with increasing NPM for all methods. MUAP amplitude showed a consistent bias (around 4% for EA and SA and 1–2% for the rest). MA, TA and SSMA had the lowest SPMF and MWP error figures. Therefore, these methods most accurately preserve and represent MUAP physiological information of utility in clinical medical practice. The other methods, particularly WA, performed noticeably worse. Convergence rate was similar for all methods, with NPM values averaged among the nine methods, which ranged from 10 to 40, depending on the waveform parameter evaluated.

EMG signal classification using ANN and ANFIS for neuro-muscular disorders

Neuro-muscular disorders can be caused by immunological and autoimmune disorders. Electromyography (EMG) may aid with the diagnosis of nerve compression, nerve rest injury, with other problems of muscles and nerves. Electromyography signal studies the electrical activity of the muscles and forms a valuable neurophysiologic test for the assessment of neuromuscular disorders. When the numbers of Motor Unit action Potentials (MUPs) are increased, it is very difficult for the neurophysiologist to distinguish the individual waveforms. Thus the classification of the EMG signal becomes necessary. The existing quantitative analysis methods have several limitations such as lower recognition rate of MUP waveforms, sensitive to continuous training, less accuracy and provide inconsistent output. In this paper, in order to classify the tasks, the EMG signals are trained using soft computing techniques like Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). ANFIS combines both neural networks and fuzzy logic principles; it can capture the benefits of both in a single framework. The principal component analysis and wavelet transform are used as dimensionality reduction methods and the classification is done by using the soft computing techniques. These proposed techniques have advantages like higher recognition rate, insensitivity to overtraining, higher reliability and accuracy.

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.

Electrophysiological evidence of doubly innervated branched muscle fibers in the human brachioradialis muscle

Objective Motor-unit action potentials (MUAPs) with unstable satellite (late-latency) components are found in EMG signals from the brachioradialis muscles of normal subjects. We analyzed the morphology and blocking behavior of these MUAPs to determine their anatomical origin. Methods EMG signals were recorded from the brachioradialis muscles of 5 normal subjects during moderate-level isometric contractions. MUAP waveforms, discharge patterns, and blocking were determined using computer-aided EMG decomposition. Results Twelve MUAPs with unstable satellite potentials were detected, always two together in the same signal. Each MUAP also had a second unstable component associated with its main spike. The blocking behavior of the unstable components depended on how close together the two MUAPs were when they discharged. Conclusions The latencies and blocking behavior indicate that the unstable components came from branched muscle fibers innervated by two different motoneurons. The satellite potentials were due to action potentials that traveled to the branching point along one branch and back along the other. The blockings were due to action-potential collisions when both motoneurons discharged close together in time. Significance Animal studies suggest that branched muscle fibers may be a normal characteristic of series-fibered muscles. This study adds to our understanding of these muscles in humans.

Effects of the physiological parameters on the signal-to-noise ratio of single myoelectric channel

BackgroundAn important measure of the performance of a myoelectric (ME) control system for powered artificial limbs is the signal-to-noise ratio (SNR) at the output of ME channel. However, few studies illustrated the neuron-muscular interactive effects on the SNR at ME control channel output. In order to obtain a comprehensive understanding on the relationship between the physiology of individual motor unit and the ME control performance, this study investigates the effects of physiological factors on the SNR of single ME channel by an analytical and simulation approach, where the SNR is defined as the ratio of the mean squared value estimation at the channel output and the variance of the estimation.MethodsMathematical models are formulated based on three fundamental elements: a motoneuron firing mechanism, motor unit action potential (MUAP) module, and signal processor. Myoelectric signals of a motor unit are synthesized with different physiological parameters, and the corresponding SNR of single ME channel is numerically calculated. Effects of physiological multi factors on the SNR are investigated, including properties of the motoneuron, MUAP waveform, recruitment order, and firing pattern, etc.ResultsThe results of the mathematical model, supported by simulation, indicate that the SNR of a single ME channel is associated with the voluntary contraction level. We showed that a model-based approach can provide insight into the key factors and bioprocess in ME control. The results of this modelling work can be potentially used in the improvement of ME control performance and for the training of amputees with powered prostheses.ConclusionThe SNR of single ME channel is a force, neuronal and muscular property dependent parameter. The theoretical model provides possible guidance to enhance the SNR of ME channel by controlling physiological variables or conscious contraction level.

P32.5 Influence of baseline fluctuation cancellation on automatic measurement of the motor unit action potential duration

The aim of this work is to analyze the influence of a method for baseline fluctuation (BLF) cancellation for electromyographic (EMG) signals on automatic methods for measurement of the motor unit action potential (MUAP) duration. These methods include four conventional automatic methods (CAMs) and a recently published wavelet transform method (WTM). A set of 182 MUAPs from 170 EMG recordings were studied. The CAMs and the WTM were applied to the MUAPs before and after applying BLF cancellation to the recordings. A gold standard of duration marker positions (GSP) ws manually established. The accuracy of each algorithm was estimated as the dfference between its positions and the GSP. Accuracies were compared for the 5 methods and for each method before and after BLF cancellation. A significant difference between accuracy preand post-BLF removal was found in two CAMs; markers were closer to the GSP after BLF removal. For all MUAPs, the differences between WTM markers and the GSP were the smallest, and significant differences were not found for the WTM before and after BLF cancellation. The management of BLF is an important issue in EMG signal processing and BLF removal must be considered in extraction and analyse of MUAP waveforms. The BLF removal method improved the performance of two CAMs for MUAP duration measurement. The WTM was the most accurate and was not affected by BLF. Ignacio Rodriguez Carreno Universidad de Navarra Depto. Metodos Cuantitativos Campus Universitario 31080 Pamplona irodriguezc@unav.es Luis Gila Useros Departamento de Neurofisiologia Clinica Hospital Virgen del Camino 31008 Pamplona luis.gila.useros@cfnavarra.es Armando Malanda Trigueros Universidad Publica de Navarra Depto. Ing. Electrica y Electronica 31006 Pamplona malanda@unavarra.es Javier Navallas Irujo Universidad Publica de Navarra Depto. Ing. Electrica y Electronica 31006 Pamplona javier.navallas@unavarra.es Javier Rodriguez Falces Universidad Publica de Navarra Depto. Ing. Electrica y Electronica 31006 Pamplona javier.rodriguez.falces@gmail.com Sagrario Gomez Elvira Universidad Publica de Navarra Depto. Estadistica e Investigacion Operativa 31006 Pamplona sgomez@unavarra.es

ART2 Neural Network for Surface EMG Decomposition

Extraction of individual Motor Unit Action Potentials (MUAPs) from a surface ElectroMyoGram (EMG) is an essential but challenging task for clinical study and physiological investigation. This paper presents an automatic decomposition of surface EMGs using a self-organised ART2 neural network. In our approach, MUAP peaks are first detected using a Weighted Low-Pass Differential (WLPD) filter. A modified ART2 network is then utilised to classify MUAPs based on MUAP waveforms and firing time information. Individual MUAP trains are identified from real surface EMG signals recorded during weak contraction, and also from simulated surface EMGs. The firing statistics and the waveforms of individual MUAPs are then extracted. A number of computer tests on 50 simulated and real surface EMGs of limb muscles show that up to five MUAP trains can be effectively extracted, with their waveforms and firing parameters estimated. Being able to decompose real surface EMGs has essentially demonstrated the potential applications of our approach to the non-invasive diagnosis of neuromuscular disorders.

A model of the muscle-fiber intracellular action potential waveform, including the slow repolarization phase.

Recent studies have shown that the slow repolarization phase or "negative afterpotential" of the intracellular muscle-fiber action potential (IAP) plays an important role in determining the shape of the extracellularly recorded motor-unit action potential (MUAP). This paper presents a model of the IAP waveform as the sum of a spike and an afterpotential, both represented by simple analytical expressions. The model parameters that specify the sizes of the spike and afterpotential are shown to be proportional to the quadrupole and dipole moments of the transmembrane current distribution associated with the spike of the wave of excitation. The model provides a computationally efficient method for simulating the MUAP, and it can be reliably inverted to estimate the model parameters from empirical IAP and MUAP waveforms.

NNERVE: neural network extraction of repetitive vectors for electromyography--Part I: Algorithm.

Artificial neural network (ANN) based signal processing methods have been shown to have significant robustness in processing complex, degraded, noisy, and unstable signals. A novel approach to automated electromyogram (EMG) signal decomposition, using an ANN processing architecture, is presented in this paper. Due to the lack of a priori knowledge of motor unit action potential (MUAP) morphology, the EMG decomposition must be performed in an unsupervised manner. An ANN classifier, consisting of a multilayer perceptron neural network and employing a novel unsupervised training strategy, is proposed. The ANN learns repetitive appearances of MUAP waveforms from their suspected occurrences in a filtered EMG signal in an autoassociative learning task. The same training waveforms are fed into the trained ANN and the output of the ANN is fed back to its input, giving rise to a dynamic retrieval net classifier. For each waveform in the data, the network discovers a feature vector associated with that waveform. For each waveform, classification is achieved by comparing its feature vector with those of the other waveforms. Firing information of each MUAP is further used to refine the classification results of the ANN classifier. Then, individual MUAP waveform shapes are derived and their firing tables are created.

Fatigue-related changes in motor unit action potentials of adult cats.

The purpose of this study was to quantify the changes in motor-unit action potentials (MUAP) and force during a standard motor-unit fatigue test. MUAP waveforms were characterized by the measurement of amplitude, duration, area, and shape (as reflected in a coefficient of proportionality). Fatigue-resistant motor units exhibited small, but statistically significant, changes in MUAP amplitude and area during the fatigue test, whereas fatigable motor units displayed variable changes in MUAP amplitude, duration, and area. For all motor-unit types, the coefficient of proportionality did not change, and hence the change in MUAP area was proportional to the combined changes in amplitude and duration. The between- and within-train changes in MUAP were also distinct for the fatigue-resistant and fatigable motor units. Although several mechanisms could be responsible for the changes in the MUAP as the fatigue test proceeded, the dissociation of the time courses for MUAP and force indicated that these MUAP changes were not the principal reason for the decline in force under these conditions.

Simulation of myopathic motor unit action potentials.

Normal motor units (MUs) were simulated and their architecture altered to simulate the changes produced by myopathy. The concentric needle electromyographic recordings of motor unit action potentials (MUAPs) from the MUs were then also simulated. These simulated MUAPs showed features that are seen in myopathy: normal amplitude and slightly reduced area, MUAPs with simple waveform and reduced duration, and complex MUAPs with normal or increased duration. The MUAP waveforms were complex because of increased variability of fiber diameter and not because of loss of muscle fibers. The MUAP duration increased when the variability of fiber diameter increased. Finally, MUAPs similar to those seen in neurogenic diseases were produced from MUs in which the only abnormality was increased variability of fiber diameter.

Simulation Techniques in Electromyography

A motor unit action potential (MUAP) recorded in clinical electromyography (EMG) is the spatial and temporal summation of the action potentials (AP's) from all muscle fibers in a motor unit (MU). An important determinant of MUAP waveform characteristics is the size of the recording electrode. In this paper, we have described the use of a modified line source model of single muscle fiber action potentials to simulate MUAP's as recorded by single fiber (SF) EMG, concentric needle (CN) EMG, and macro-EMG electrodes. Results indicate that SFEMG recordings from a normal MU contain mainly the AP's of the closest one to three muscle fibers of the MU. The amplitude, area, and duration of the simulated CNEMG MUAP's are determined mainly by the number and size of muscle fibers within a semicircular territory of 0.5, 1.5, and 2.5 mm, respectively, around the tip of the electrode. The amplitude and area of simulated macro-EMG MUAP's increase with the number of muscle fibers in the MU.

A surface electrode array for detecting action potential trains of single motor units

Action potentials of single motor units were detected by a linear surface electrode array placed perpendicular to the longitudinal axis of the biceps brachii. Twelve myoelectric signals were derived simultaneously from a voluntarily contracting muscle. Using a visual feedback control, 3 subjects produced spike trains of single motor unit action potentials (MUAPs) at a weak contraction. When the myoelectric signals showed an interference pattern at a moderate contraction, several MUAPs were isolated by a visual analysis. MUAPs occurring at about fixed intervals with constant amplitudes and with identical wave forms were presumed to be the action potential trains of single motor units. Reliable estimates of single MUAP wave forms were obtained by averaging and superimposing the detected signals at a timing of characteristic potential peaks. Then not only the firing rate of the spikes but also the territory and the wave form of single MUAPs were investigated. Most MUAPs had a sharp and symmetrical distribution of potentials on a skin surface along the muscle circumference, while some MUAPs showed complex wave forms with some separate potential peaks. The possible arrangement of muscle fibers belonging to the motor units was estimated from the MUAP wave forms.