The Effect of Electromyographic Jitter on Single Motor Unit EMG Potentials

Variability between successive discharges of the single motor unit potential in the biceps brachii muscle, due to electromyographic (EMG) jitter, has been investigated. This jitter results from random arrival times of single fiber potentials at the electrode. A computer model has been used to generate single motor unit potentials incorporating the effects of EMG jitter. A computed variance peak was found in the fast rising edge of the motor unit potential for electrode sites outside of the motor unit territory. This peak was also observed in experimental data recorded from human subjects. The peak variance outside of the motor unit territoxy has also been mathematically related to the number of fibers in the motor unit, jitter, and the slope of the mean action potential at the center of the fast rising edge.     

Interscale wavelet maximum-a fine to coarse algorithm for wavelet analysis of the EMG interference pattern

A method has been developed, interscale wavelet maximum (ISWM), for characterising the electromyogram (EMG) interference pattern to assist in the diagnosis of neuromuscular disease. EMG signals are decomposed with the redundant dyadic wavelet transform and wavelet maxima (WM) are found. Thresholding methods are applied to remove WM due to noise and background activity. An efficient fine-to-coarse algorithm identifies the WM tree structure for the motor unit action potential rising edges. The WM for each tree are summed at each scale; the largest value is the ISWM. Highly significant differences in ISWM values have been found between healthy, myopathic, and neuropathic subjects that could make the technique a useful diagnostic tool.     

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.     

Evaluation of the Effectiveness of EMG Parameters in the Study of Neurogenic Diseases-A Statistical Approach Using Clinical and Simulated Data

Of interest here is the problem of determining to what extent combinations of parameters derived from the EMG signal allow 1) discriminating two subclasses of neurogenic myopathies, and 2) recognizing different morphologies of the motor unit action potential underlying a measured EMG signal. EMG signals measured on clinical subjects and computer-simulated EMG signals were collected in a database and used cooperatively in this study. Suitable statistical models were developed which allow testing hypotheses on the role of accepted EMG parameters for the two purposes named above, and deriving new suitable combinations of EMG parameters. Results support the hypothesis that frequency-domain parameters are very clearly related to the morphology of the motor unit action potential. However, the attempt to use them in order to discriminate the two pathologic subclasses considered appears to be jeopardized by the fact that the signal may be measured in territories which do not reflect the morphology of the motor unit action potential dominant in such subclasses. On the basis of time-domain parameters, a significant discrimination was obtained between the two subclasses, and such discrimination is related mainly to a time-domain parameter which has already proved successful in the discrimination between myopathic and normal subjects. Data corroborate the hypothesis that the diagnostic yield improves when time-domain EMG parameters are measured at recruitment.     

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.     

Use of Signal Representation to Identify Abnormal Motor Unit Potentials in Macro EMG

Macro electromyography (EMG) is a recently described recording technique allowing a nonselective recording of motor unit activity. The pick-up area of the electrode, the cannula of a modified single-fiber electrode, covers the entire motor unit territory. The motor unit potential (MUP) is obtained by averaging the cannula signals that are time locked to a single-fiber action potential. The MUP waveform is relatively insensitive to the position of the electrode within the motor unit. The amplitude of the MUP has been used as a measure of the size of the motor unit and normal values for the individual and median MUP amplitude have been defined.     

An algorithm for sequential signal estimation and system identification for EMG signals

This paper presents a new algorithm for optimal adaptation of the signal templates of a matched filter bank used in the detection of the motor unit action potential waveforms (abbreviated as MUAP's) in an electromyogram (EMG). It is of interest, for clinical diagnosis and therapy, to detect as many MUAP's as possible in a single measurement, and to determine for each motor unit the repetition. rate of its respective MUAP. For this purpose, we have developed a computer program which, in addition to other subprograms, contains the adaptive filter bank mentioned above. The templates in this fllter bank have to be adapted to nonpredetermined changes in measurement conditions such as the movement of the needle electrode inserted in the muscle. In the present paper, the above templates are estimated by means of a "tumbling algorithm," so called because the successive MUAP's from a given motor unit are used as noisy data vectors in a time-varying Kalman filter-predictor framework, which alternately estinates their evolving shapes and identifies the time-varying parameters of the model generating them. The algorithm has been applied with success to synthetic and real EMG data.     

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.     

Spatial Filtering of Noninvasive Multielectrode EMG: Part I?Introduction to Measuring Technique and Applications

Complementary to its conventional applications, surface EMG is also suited to gain more detailed information on the functional state of a muscle, when measurement configurations with smaller pickup areas are used. A new category of suitable measurement configurations is obtained by application of the spatial filtering principle to electromyography. In a spatial filter unit, the signals of several recording electrodes are combined to form one output signal channel. The filter characteristic is determined by the weighting factors used and by the geometrical arrangement of the electrodes. Extended multielectrode arrays and multichannel recording make possible the detection of correlated excitations at different sites of the muscle. Even in high levels of muscle contraction, single motor unit impulses that are suitably shaped by filtering can be repeatedly recognized in the surface EMG signal. In clinical studies, pathologically shaped impulses have been identified indicating multiple innervation zones. The initiation and the propagation of excitation within single motor units can be detected with improved accuracy even from very small muscles.     

On Modeling the Single Motor Unit Action Potential

A model has been proposed for the generation of single motor unit potentials routinely observed in the clinical EMG examination of the normal biceps brachii muscle. A dipole representation was chosen for the single fiber activity. The motor unit was constructed from a uniform random array of single fibers. Motor unit potentials generated by this array have been observed at various distances both inside and outside the array. The effects of single fiber dipole axial dispersions on the potentials observed at increasing distances from the array have also been investigated. Motor unit potentials generated by the model have been compared with existing data from multielectrode studies in the biceps brachii.     

A New Framework and Computer Program for Quantitative EMG Signal Analysis

Techniques for analyzing electromyographic signals, which estimate and detect potentials caused by active motor units in human striated muscles, are described. A framework within which these techniques are incorporated into a computer program for the quantitative analysis of EMG signals is then proposed. The resulting program allows the diagnosis of neurogenic and myogenic diseases by analyzing the waveforms of the motor unit potentials (MUP's). It also permits the research of the healthy and disturbed neuromuscular control loop by analyzing the point processes given by the activation of the single motor units.     

A fast and reliable technique for muscle activity detection from surface EMG signals

The estimation of on-off timing of human skeletal muscles during movement is an important issue in surface electromyography (EMG) signal processing with relevant clinical applications. In this paper, a novel approach to address this issue is proposed. The method is based on the identification of single motor unit action potentials from the surface EMG signal with the use of the continuous wavelet transform. A manifestation variable is computed as the maximum of the outputs of a bank of matched filters at different scales. A threshold is applied to the manifestation variable to detect EMG activity. A model, based on the physical structure of the muscle, is used to test the proposed technique on synthetic signals with known features. The resultant bias of the onset estimate is lower than 40 ms and the standard deviation lower than 30 ms in case of additive colored Gaussian noise with signal-to-noise ratio as low as 2 dB. Comparison with previously developed methods was performed, and representative applications to experimental signals are presented. The method is designed for a complete real-time implementation and, thus, may be applied in clinical routine activity.     

Spectral Analysis of Surface Motor Unit Action Potentials and Surface Interference Electromyogram

Surface interference electromyograms (EMG's) were recorded from the tibial muscle of a healthy subject during 50 percent maximal contraction and single motor unit action potentials (MUAP's) were isolated by averaging from the interference pattern. The formation of the EMG was simulated by summing isolated MUAP's according to the statistical properties of the corresponding motor unit discharges. Power spectral density functions (PSDF's) were finally computed for single MUAP's as well as for simulated and experimental EMG's and compared with each other.     

Nonlinear Spatial Filtering of Multichannel Surface Electromyogram Signals During Low Force Contractions

This study introduces the application of nonlinear spatial filters to help identify single motor unit discharge from multiple channel surface electromyogram (EMG) signals during low force contractions. The nonlinear spatial filters simultaneously take into account the instantaneous amplitude and frequency information of a signal. This property was used to enhance motor unit action potentials (MUAPs) in the surface EMG record. The advantages of nonlinear spatial filtering for surface MUAP enhancement were investigated using both simulation and experimental approaches. The simulation results indicate that when compared with various linear spatial filters, nonlinear spatial filtering achieved higher SNR and higher kurtosis of the surface EMG distribution. Over a broad range of SNR and kurtosis levels for the input signal, nonlinear spatial filters achieved at least 32 times greater SNR and 11% higher kurtosis for correlated noise, and at least 15 times greater SNR and 1.7 times higher kurtosis for independent noise, across electrode array channels. The improvements offered by nonlinear spatial filters were further documented by applying them to experimental surface EMG array recordings. Compared with linear spatial filters, nonlinear spatial filters achieved at least nine times greater SNR and 25% higher kurtosis. It follows that nonlinear spatial filters represent a potentially useful supplement to linear spatial filters for detection of motor unit activity in surface EMG at low force contractions.     

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.     

Decomposition of multiunit electromyographic signals

We have developed a comprehensive technique to identify single motor unit (SMU) potentials and to decompose overlapped electromyographic (EMG) signals into their constituent SMU potentials. This technique is based on one-channel EMG recordings and is easily implemented for many clinical EMG tests. There are several distinct features of our technique: 1) it measures waveform similarity of SMU potentials in the wavelet domain, which gives this technique significant advantages over other techniques; 2) it classifies spikes based on the nearest neighboring algorithm, which is less sensitive to waveform variation; 3) it can effectively separate compound potentials based on a maximum signal energy deduction algorithm, which is fast and relatively reliable; and 4) it also utilizes the information on discharge regularities of SMU's to help correct possible decomposition errors. The performance of this technique has been evaluated by using simulated EMG signals composed of up to eight different discharging SMU's corrupted with white noise, and also by using real EMG signals recorded at levels up to 50% maximum voluntary contraction. We believe that it is a very useful technique to study SMU discharge patterns and recruitment of motor units in patients with neuromuscular disorders in clinical EMG laboratories.     

A Nonstationary Model for the Electromyogram

A theoretical model of the electromyographic (EMG) signal has been developed. In the model, the neural pulse train inputs were considered to be point processes which passed through linear, time-invariant systems that represented the respective motor unit action potential. The outputs were then summed to produce the EMG. It was assumed, that in the production of muscle force, the controlled parameter was the number of active motor units, n(t). The model then showed that the EMG can be represented as an amplitude modulation process of the form EMG = [Kn(t)1/2 w(t) with the stochastic process, w(t), having the spectral and probability characteristics of the EMG during a constant contraction. Various assumptions made in the model development have been verified by experiments.     

A Spectral Analysis of Single Motor Unit Potentials from Human Extraocular Muscle

A spectral analysis of single motor unit potentials from the human medial rectus extraocular muscle is described. The analysis was performed by first averaging individual single motor unit potentials and then subjecting the average to Fourier analysis using a digital computer. The portion of the spectrum between 300 Hz and 2.8 kHz has definite bandpass characteristics with -3-dB points for the low-and high-frequency sides equal to 470 Hz and 1.4 kHz, respectively. The spectrum provides some insight into the characteristics of a filter that would provide an indication of the occurrence of single unit potentials in an interference electromyogram.     

Singularity characteristics of needle EMG IP signals

Clinical electromyography (EMG) interference pattern (IP) signals can reveal more diagnostic information than their constituents, the motor unit action potentials (MUAPs). Singularities and irregular structures typically characterize the mathematically defined content of information in signals. In this paper, a wavelet transform method is used to detect and quantify the singularity characteristics of EMG IP signals using the Lipschitz exponent (LE) and measures derived from it. The performance of the method is assessed in terms of its ability to discriminate healthy, myopathic and neuropathic subjects and how it compares with traditionally used Turns Analysis (TA) methods and a method recently developed by the authors, interscale wavelet maximum (ISWM). Highly significant intergroup differences were found using the LE method. Most of the singularity measures have a performance similar to that of ISWM and considerably better than that of TA. Some measures such as the ratio of the mean LE value to the number of singular points in the signal have considerably superior performance to both methods. These findings add weight to the view that wavelet analysis methods offer an effective way forward in the quantitative analysis of EMG IP signal to assist the clinician in the diagnosis of neuromuscular disorders.     

二维级联流水结构大点数FFT运算器实现研究

大点数快速傅里叶变换(FFT)运算在雷达、通信信号侦察中有广泛应用,其基于现场可编程门阵列(FPGA)的实现方法有重要的研究价值。推导出点数为N的大点数FFT运算分解为2级小点数FFT运算级联的运算公式,在此基础上给出其实现步骤,从流水线结构设计、基本运算单元以及地址生成等方面详细介绍一维列(行)变换的工程实现方法,并给出列、行变换之间所乘旋转因子的压缩算法。工程实际应用表明,该大点数FFT运算器具有变换速度快、调试方便及可在单片FPGA实现的优点。