Simulated Motor Unit Action Potentials Research Articles

Ability of spatial filters to distinguish between two MUAPs generated from MUs with different locations, sizes and fibers pennation

In this study, we investigated the effects of the motor unit (MU) location and size and the fibres pennation on the ability of anisotropic and almost isotropic spatial filters used to detect surface electromyographic (EMG) signals to make a distinction between motor unit action potentials (MUAPs) generated from two MUs. The study was based on simulated MUAPs. The fibres orientation was performed by varying the fibres pennation angle (FPA).The root mean square error (RMSE) between MUAPs generated from two MUs was used as a criterion to evaluate the ability of the investigated filters to distinguish between two generated MUAPs. The location of a MU was fixed and the second MU moved away from the first MU in the transversal direction for the first case and in the depth direction in the second case to take five different locations in every case.We showed that the capability of the studied filters to more separate two MUAPs strongly depended on MU location, MU size and FPA. This capability of separation was best with large distances between the two MUs and with large sizes of them. Furthermore, the main survey of this work was that the BiTDD filter has the best ability of separation of two MUAPs than the other filters in a given FPA interval. The number of pennation angles in this interval is related to the location and size of the moved MU.

Electrodes Adaptive Model in Estimating the Depth of Motor Unit: A Motor Unit Action Potential Based Approach.

High-density surface electromyography (EMG) has been proposed to overcome the lower selectivity with respect to needle EMG and to provide information on a wide area over the considered muscle. Motor units decomposed from surface EMG signal of different depths differ in the distribution of action potentials detected in the skin surface. We propose a noninvasive model for estimating the depth of motor unit. We find that the depth of motor unit is linearly related to the Gaussian RMS width fitted by data points extracted from motor unit action potential. Simulated and experimental signals are used to evaluate the model performance. The correlation coefficient between reference depth and estimated depth is 0.92 ± 0.01 for simulated motor unit action potentials. Due to the symmetric nature of our model, no significant decrease is detected during the electrode selection procedure. We further checked the estimation results from decomposed motor units, the correlation coefficient between reference depth and estimated depth is 0.82 ± 0.07. For experimental signals, high discrimination of estimated depth vector is detected across gestures among trials. These results show the potential for a straightforward assessment of depth of motor units inside muscles. We discuss the potential of a non-invasive way for the location of decomposed motor units.

Motor unit innervation zone localization based on robust linear regression analysis

With the aim of developing a flexible and reliable procedure for superficial muscle innervation zone (IZ) localization, we proposed a method to estimate IZ location using surface electromyogram (EMG) based on robust linear regression. Regression lines were used to model the bidirectional propagation pattern of a single motor unit action potential (MUAP) and visualize the trajectory of the MUAP propagation. IZ localization was performed by identifying the origin of the bidirectional MUAP propagation. Robust linear regression and MUAP peak detection, combined with propagation phase reversal identification, may provide an efficient way to estimate IZ location. Our method offers high resolution in locating IZs based on simulation studies and experimental tests. Furthermore, our method is flexible and may also be applied using a relatively small number of EMG channels. A comparative study of the proposed method with the cross-correlation method for IZ localization was conducted. The results obtained with simulated MUAPs and measured spontaneous MUAPs in the biceps brachii muscle in six subjects (four males and two females, 57 ± 10 years old) with amyotrophic lateral sclerosis (ALS). Our method achieved estimation performance comparable to that obtained by using the cross-correlation method but with higher resolution. This study provides an accurate and practical method to estimate IZ location.

Simulations of high-density surface electromyograms in dynamic muscle contractions.

We describe the extension of pre-existing cylindrical volume conductor model to synthetic high-density surface electromyograms (hdEMG), simulated during dynamic contractions of fusiform skeletal muscles. Its modular structure comprises two main parts. First, dynamic changes of motor unit action potentials (MUAPs) during 36 discrete steps of muscle shortening are simulated. Second, the increase in depth of simulated motor units (MUs) due to shortening and thickening of muscle fibers is simulated. MU firing patterns are generated with the model proposed by Fuglevand et al. and convolved with simulated MUAPs. In this way, the hdEMG simulator can be used to generate dynamic hdEMG of arbitrary muscle shortening, thickening and excitation profiles. In order to demonstrate the value of the aforementioned simulator we independently analyze the impact of muscle shortening and muscle thickening on MU identification by Convolution Kernel Compensation (CKC) technique.

The effect of recording site on extracted features of motor unit action potential

Motor unit action potential (MUAP), which consists of individual muscle fiber action potentials (MFAPs), represents the electrical activity of the motor unit. The values of the MUAP features are changed by denervation and reinnervation in neurogenic involvement as well as muscle fiber loss with increased diameter variability in myopathic diseases. The present study is designed to investigate how increased muscle fiber diameter variability affects MUAP parameters in simulated motor units. In order to detect this variation, simulated MUAPs were calculated both at the innervation zone where the MFAPs are more synchronized, and near the tendon, where they show increased temporal dispersion. Reinnervation in neurogenic state increases MUAP amplitude for the recordings at both the innervation zone and near the tendon. However, MUAP duration and the number of peaks significantly increased in a case of myopathy for recordings near the tendon. Furthermore, of the new features, “number of peaks×spike duration” was found as the strongest indicator of MFAP dispersion in myopathy. MUAPs were also recorded from healthy participants in order to investigate the biological counterpart of the simulation data. MUAPs which were recorded near to tendon revealed significantly prolonged duration and decreased amplitude. Although the number of peaks was increased by moving the needle near to tendon, this was not significant.

Efficient parameterization of MUAP signal for identification of MU and volume conductor characteristics using neural networks

The motor unit action potential (MUAPs) shapes depend on the anatomy and the physiology of the contracted muscle. The aim of this work is the identification of some characteristics of the motor unit (MU) and the volume conductor, namely the MU depth, the innervation zone width and the thickness of fat and skin layers based on MUAP signal parameters. The relationship between these characteristics and MUAP parameters are non-linear and complex. Thus, the use of the neural networks approach becomes an efficient tool to put in evidence this relationship. We have used the similarity and the homogeneity of the parameter criterions to choose which parameters are appropriate for the extraction. Two identification systems are presented and compared, a global system and a separate one. In order to evaluate the performance of each system, we have tested them using several simulated MUAP signals corrupted with additive Gaussian noise at different signal to noise ratios (SNR). A new test is introduced in which the electrode radius, the bar electrode dimensions and inclination angles for the detection system, fixed during the training process, are changed.

The size index as a motor unit identifier in electromyography examined by numerical calculation

A computer simulation was performed to investigate the size index as a motor unit identifier in electromyography. The size index calculated from the amplitude and area of the simulated motor unit action potential (MUP) was plotted against the distance between the needle electrode and current source to show how the index changes as a function of the distance. The index of the MUP also was plotted against the number of muscle fibers belonging to a single motor unit, the size of the motor unit territory, and the diameter of the muscle fibers in order to establish the major determinants of the index. The index was relatively constant for the distance less than 2 mm between the needle electrode and closest edge of the current source. It changed logarithmically with the number of muscle fibers and with the diameter of the fibers.

Detecting single fiber contributions to motor unit action potentials.

The ability to detect muscle fiber action potential (MFAP) contributions to motor unit action potentials (MUAPs) measured using single fiber (SF) and concentric needle (CN) electrodes was studied using simulated MUAPs. Various MFAP-acceleration thresholds were used to define significant fiber contributions. Attempts to detect the significant MFAP contributions, by locating peaks in filtered MUAPs or MUAP accelerations using various MUAP-based thresholds, were then made. Considering filtered MUAPs and a significant contribution threshold of 7.5 kV/s2, and using fiber-density peak-detection criteria, at best 46% and 50% of significant MFAP contributions were detected for the SF and CN MUAPs, respectively. Considering MUAP accelerations and a significant contribution threshold of 7.5 kV/s2, 80% and 84% of significant MFAP contributions could be detected, respectively. Most significant contributions were created from fibers located within approximately 350 microm of the electrode. The results suggest that significant peaks, defined using MUAP-based thresholds, within the acceleration of CN MUAPs can strongly correspond to individual fiber activity and may be useful for measuring fiber density and neuromuscular jitter.

P125 The reorganization of motor units (MUs) in amyotrophic lateral sclerosis (ALS) in comparison to spinal muscular atrophy (SMA)

Motor unit action potential (MUAP), which consists of individual muscle fiber action potentials (MFAPs), represents the electrical activity of the motor unit. The values of the MUAP features are changed by denervation and reinnervation in neurogenic involvement as well as muscle fiber loss with increased diameter variability in myopathic diseases. The present study is designed to investigate how increased muscle fiber diameter variability affects MUAP parameters in simulated motor units. In order to detect this variation, simulated MUAPs were calculated both at the innervation zone where the MFAPs are more synchronized, and near the tendon, where they show increased temporal dispersion. Reinnervation in neurogenic state increases MUAP amplitude for the recordings at both the innervation zone and near the tendon. However, MUAP duration and the number of peaks significantly increased in a case of myopathy for recordings near the tendon. Furthermore, of the new features, “number of peaks × spike duration” was found as the strongest indicator of MFAP dispersion in myopathy. MUAPs were also recorded from healthy participants in order to investigate the biological counterpart of the simulation data. MUAPs which were recorded near to tendon revealed significantly prolonged duration and decreased amplitude. Although the number of peaks was increased by moving the needle near to tendon, this was not significant.

Model for decomposition of the motor unit action potential. 2. Analysis of simulated motor unit action potentials.

A model for decomposition of the motor unit action potential (MUAP) which finds an optimal fit of a set of simulated single-fibre action potentials (SSFAPs) to the original MUAP is tested. The composition of SSFAPs which best produces the MUAP is assumed to carry information about the actual distribution of single-fibre action potentials generating the MUAP. Two methods are derived from the model. The first makes use of a fixed set of SSFAPs. In the second method, a gradually expanding set of SSFAPs is built, using a sequence of crosscorrelation, optimal reconstruction and subtraction. In the paper MUAPs are constructed under various well defined conditions. The MUAPs are decomposed by the two methods, and the results are compared with traditional MUAP parameters. Under these conditions, the model obtains parameters with closer biological connections compared with traditional measures.

A study of the motor unit action potential by means of computer simulation.

In order to study the motor unit action potential a computer stimulation model was developed. It is based on the superposition of single muscle fibre potentials of the fibres belonging to the motor unit. The parameters which characterize each fibre (spatial position, diameter, and a dispersion of arrival time of the potential at the electrode) are chosen from statistical distributions which can be derived from anatomical and physiological data. The electrode type, position and dimensions can be specified. Simulated motor unit action potentials are presented in the time and frequency domain. The stimulation results refer to (1) the influence of the electrode position and dimensions with respect to the motor unit territory, (2) the meaning of this model for the study of pathological phenomena, (3) the variability of some parameters characterizing the motor unit, (4) the selectivity of uni- and bipolar electrodes and finally (5) the influence of the geometrical situation of the motor end-plates within the muscle, on the shape of motor unit action potentials.