A novel approach for estimating muscle fiber conduction velocity by spatial and temporal filtering of surface EMG signals

We describe a new method for the estimation of muscle fiber conduction velocity (CV) from surface electromyography (EMG) signals. The method is based on the detection of two surface EMG signals with different spatial filters and on the compensation of the spatial filtering operations by two temporal filters (with CV as unknown parameter) applied to the signals. The transfer functions of the two spatial filters may have different magnitudes and phases, thus the detected signals have not necessarily the same shape. The two signals are first spatially and then temporally filtered and are ideally equal when the CV value selected as a parameter in the temporal filters corresponds to the velocity of propagation of the detected action potentials. This approach is the generalization of the classic spectral matching technique. A theoretical derivation of the method is provided together with its fast implementation by an iterative method based on the Newton's method. Moreover, the lowest CV estimate among those obtained by a number of filter pairs is selected to reduce the CV bias due to nonpropagating signal components. Simulation results indicate that the method described is less sensitive than the classic spectral matching approach to the presence of nonpropagating signals and that the two methods have similar standard deviation of estimation in the presence of additive, white, Gaussian noise. Finally, experimental signals have been collected from the biceps brachii muscle of ten healthy male subjects with an adhesive linear array of eight electrodes. The CV estimates depended on the electrode location with positive bias for the estimates from electrodes close to the innervation or tendon regions, as expected. The proposed method led to significantly lower bias than the spectral matching method in the experimental conditions, confirming the simulation results.     

Spatial Filtering of Noninvasive Multielectrode EMG: Part II?Filter Performance in Theory and Modeling

Spatial filtering, particularly common in the field of engineering, is adapted in theory and practice to the filtering of propagating spatial EMG signals. This technique offers a new flexibility in the design of selective EMG measurement configurations. Longitudinal as well as two-dimensional spatial filters can be used. The conditions for the design of suitable spatial filters are deduced by signal theory. The performances of different selected configurations are compared by means of a given simple model of an excited motor unit. The modeling results compare well to the previously described experimental signals.     

Selectivity of spatial filters for surface EMG detection from the tibialis anterior muscle

Many spatial filters have been proposed for surface electromyographic (EMG) signal detection. Although theoretical and modeling predictions on spatial selectivity are available, there are no extensive experimental validations of these techniques based on single motor unit (MU) activity detection. The aim of this study was to compare spatial selectivity of one- and two-dimensional (1-D and 2-D) spatial filters for EMG signal detection. Intramuscular and surface EMG signals were recorded from the tibialis anterior muscle of ten subjects. The simultaneous use of intramuscular wire and surface recordings (with the spike triggered averaging technique) allowed investigation of the activity of single MUs at the skin surface. The surface EMG signals were recorded with a grid of point electrodes (3 x 3 electrodes) and a ring electrode system at 15 locations over the muscle, with the wires detecting signals from the same intramuscular location. For most subjects, it was possible to classify, from the intramuscular recordings, the activity of the same MUs for all the contractions. The surface EMG signals were averaged with the intramuscularly detected MU action potentials as triggers. In this way, eight spatial filters--longitudinal and transversal, single and double differential (LSD, TSD, LDD, TDD), Laplacian (NDD), inverse binomial filter of the second order (IB2), inverse rectangle filter (IR), and differential ring system (C1)--could be compared on the basis of their spatial selectivity. The distance from the source (transversal with respect to the muscle fiber orientation) after which the surface detected potential did not exceed +/- 5% of the maximal peak-to-peak amplitude (detection distance) was statistically smaller for the 2-D systems and TDD than for the other filters. The MU action potential duration was significantly shorter with LDD and with the 2-D systems than with the other filters. The 2-D filters investigated (including C1) showed very similar performance and were, thus, considered equivalent from the point of view of spatial selectivity.     

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.     

Entropy-based optimization of wavelet spatial filters

A new class of spatial filters for surface electromyographic (EMG) signal detection is proposed. These filters are based on the 2-D spatial wavelet decomposition of the surface EMG recorded with a grid of electrodes and inverse transformation after zeroing a subset of the transformation coefficients. The filter transfer function depends on the selected mother wavelet in the two spatial directions. Wavelet parameterization is proposed with the aim of signal-based optimization of the transfer function of the spatial filter. The optimization criterion was the minimization of the entropy of the time samples of the output signal. The optimized spatial filter is linear and space invariant. In simulated and experimental recordings, the optimized wavelet filter showed increased selectivity with respect to previously proposed filters. For example, in simulation, the ratio between the peak-to-peak amplitude of action potentials generated by motor units 20 degrees apart in the transversal direction was 8.58% (with monopolar recording), 2.47% (double differential), 2.59% (normal double differential), and 0.47% (optimized wavelet filter). In experimental recordings, the duration of the detected action potentials decreased from (mean +/- SD) 6.9 +/- 0.3 ms (monopolar recording), to 4.5 +/- 0.2 ms (normal double differential), 3.7 +/- 0.2 (double differential), and 3.0 +/- 0.1 ms (optimized wavelet filter). In conclusion, the new class of spatial filters with the proposed signal-based optimization of the transfer function allows better discrimination of individual motor unit activities in surface EMG recordings than it was previously possible.     

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.     

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.     

Optimal FIR pre- and postfilters for decimation and interpolationof random signals

A technique for the design of finite-impulse-response (FIR) filters for decimation and interpolation in multirate systems is introduced. FIR prefilters and postfilters that jointly minimize a frequency-weighted mean-square error between the original and reconstructed signals can be designed. There is no need for ideal filter prototypes: the optimal pre-postfilter pair is determined from the signal and noise spectra and the up-sampling and down-sampling factors. Some examples of image and speech processing show that the mean square optimal filter pair leads to typical SNR improvements of 2-6 dB, in comparison to other commonly used filters     

Improvement of spatial resolution in surface-EMG: a theoretical andexperimental comparison of different spatial filters

In the present study, different isotropic and anisotropic filters have been compared by means of theoretical field simulations and experiments in volunteers. A tripole model for an excited motor unit (MU) was used as the basis for simulating the spatial extension of the filter response for each of the investigated filters. The spatial extension is an indicative of the spatial resolution. For the experimental validation, the total number of single motor units was not directly investigated, but the signal-to-noise ratio (SNR) has been determined. Therefore, the potential distribution generated on the skin surface during maximum voluntary contraction has been simultaneous spatially filtered with each of the investigated filters. The simulations show that an isotropic spatial filtering procedure reduces the spatial extension of the filter response and improves the spatial resolution of the electromyography (EMG)-recording arrangement in comparison to anisotropic spatial filters up to 30%. In other words, the spatial selectivity of the arrangement is increased. This improvement in the filter performance is more pronounced for MU's located close to the skin surface than for MU's more distantly located. Additionally, this theoretical improvement in selectivity depends on the direction of the excitation spread relative to the filter alignment. However, the investigations also show that isotropic filters offer an advantage, compared to anisotropic filters, only when the investigated MU is located extremely close to the filter input. The results of the simulations can be confirmed by the experimental investigations. An improvement of 11% in the SNR, relative to anisotropic spatial filters, can be established when using an isotropic spatial filter. This experimental improvement in selectivity is less than the theoretical improvement because the experimentally investigated MU's have less portion in the anisotropic range of the filters than the simulated one at best     

空间滤波测速仪中差分滤波器结构的优化

空间滤波测速系统中输出信号的基底成分,理论上可以通过差分方法完全消除。但由于由被测物体表面各处散射到光学系统并被探测器接收到的光能量不一致等原因,导致输出信号中仍然有基底成分残留,为信号特别是低频信号的探测带来困难。为了消除差分滤波器输出信号中残留的基底成分,对空间滤波测速仪中的图像传感器型差分滤波器的结构进行了优化设计。列出并分析了图像传感器型差分滤波器的各种可能结构形式,并且选择了几种典型的结构进行了实验。分别在均匀照明和非均匀照明条件下,从功率谱和信噪比两个方面分析了输出信号的质量,为滤波器结构的优化提供了依据。通过对比不同滤波器结构下系统输出信号的质量,得到了一种最为合适的能大大消除输出信号中残留的基底成分的滤波器结构。     

Estimation of muscle fiber conduction velocity with a spectral multidip approach

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

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

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

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.     

Concentric-ring electrode systems for noninvasive detection ofsingle motor unit activity

New recording techniques for detecting surface electromyographic (EMG) signals based on concentric-ring electrodes are proposed in this paper. A theoretical study of the two-dimensional (2-D) spatial transfer function of these recording systems is developed both in case of rings with a physical dimension and in case of line rings. Design criteria for the proposed systems are presented in relation to spatial selectivity. It is shown that, given the radii of the rings, the weights of the spatial filter can be selected in order to improve the rejection of low spatial frequencies, thus increasing spatial selectivity. The theoretical transfer functions of concentric systems are obtained and compared with those of other detection systems. Signals detected with the ring electrodes and with traditional one-dimensional and 2-D systems are compared. The concentric-ring systems show higher spatial selectivity with respect to the traditional detection systems and reduce the problem of electrode location since they are invariant to rotations. The results shown are very promising for the noninvasive detection of single motor unit (MU) activities and decomposition of the surface EMG signal into the constituent MU action potential trains.     

Spatial filtering for robust myoelectric control

Pattern recognition techniques have been applied to extract information from electromyographic (EMG) signals that can be used to control electrical powered hand prostheses. In this paper, optimized spatial filters that enhance separation properties of EMG signals are investigated. In particular, different multiclass extensions of the common spatial patterns algorithm are applied to high-density surface EMG signals acquired from the forearms of ten healthy subjects. Visualization of the obtained filter coefficients provides insight into the physiology of the muscles related to the performed contractions. The CSP methods are compared with a commonly used pattern recognition approach in a six-class classification task. Cross-validation results show a significant improvement in performance and a higher robustness against noise than commonly used pattern recognition methods.     

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.     

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.     

Vector FIR-median hybrid filters for multispectral signals

The concept of a vector median (VM) using linear substructures is introduced for multispectral signals. It is shown that the VM filter utilises the correlation between the spectral components, which is an important property when the edge shape and location should be preserved for each signal component simultaneously. The vector median approach is applied to FIR-median hybrid (FMH) filters. These filters are shown to have good noise attenuation and a low bias error near edges. Vector median filters can be applied, for example, to standard colour images in TV systems and to multispectral satellite images     

Switchable Coherence-Free Microwave Photonic Notch Filter Using a Pair of Intensity Modulators

A simple switchable coherence-free microwave photonic filter configuration is presented. It is based on a pair of intensity modulators modulating an optical signal at different times. The design is free from the velocity mismatch problem of an electrooptic modulator. This all-optical structure realizes two-tap filters with the capability of switching the sign of one of the taps to positive or negative. The sign depends on whether the linear slopes of the transfer functions of the two intensity modulators at their respective bias points are the same or opposite. The case of a phase shift between the two radio-frequency drive signals is also considered. Measured results confirm the theoretical expressions and demonstrate a stable switchable notch filter response.     

Electromyogram amplitude estimation with adaptive smoothing windowlength

Typical electromyogram (EMG) amplitude estimators use a fixed window length for smoothing the amplitude estimate. When the EMG amplitude is dynamic, previous research suggests that varying the smoothing length as a function of time may improve amplitude estimation. This paper develops optimal time-varying selection of the smoothing window length using a stochastic model of the EMG signal. Optimal selection is a function of the EMG amplitude and its derivatives. Simulation studies, in which EMG amplitude was changed randomly, found that the "best" adaptive filter performed as well as the "best" fixed-length filter. Experimental studies found the advantages of the adaptive processor to be situation dependent. Subjects used real-time EMG amplitude estimates to track a randomly-moving target. Perhaps due to task difficulty, no differences in adaptive versus fixed-length processors were observed when the target speed was fast. When the target speed was slow, the experimental results were consistent with the simulation predictions. When the target moved between two constant levels, the adaptive processor responded rapidly to the target level transitions and had low variance while the target dwelled on a level.