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.     

Signal strength versus cuff length in nerve cuff electrode recordings

When a nerve cuff electrode is used for the recording of signals from peripheral nerves, cuff dimensions have to be chosen. Traditionally, the peak-to-peak amplitude of the single-fiber action potential (SFAP) is optimized through the choice of cuff diameter and cuff length. In this paper, the dependency of the root-mean-square (RMS) value of the nerve signal on the cuff dimensions was studied and compared with the peak-to-peak value of the SFAP. A simple approximation for signal optimization by cuff dimensioning is suggested. The results were obtained from modeled SFAPs and from the electroneurogram (ENG) created by superimposed SFAPs, obtained from an inhomogeneous volume conductor model. The results show that the RMS value of the nerve signal is considerably more sensitive to the cuff length than the SFAP peak-to-peak amplitude, and that the RMS of the ENG is a linear function of the fiber diameter.     

Automatic decomposition of selective needle-detected myoelectricsignals

A procedure for the storage and documentation of myoelectric signals has been developed that consists of a selective needle signal detection protocol, a data collection-compression routine, an adaptive signal decomposition algorithm, and an error filter. The collection-compression routine stores only fixed-length signal epochs that contain motor unit action potentials (MUAPs) detected during individual motor unit firings. The decomposition algorithm assigns the collected MUAPs to candidate motor units, based on template matching using power-spectrum domain features and firing-time criteria calculated from the motor units' firing statistics. Power spectrum features allow the use of Nyquist sampling rates and remove the need for template alignment. The algorithm is adaptive and attempts to minimize dependent errors. The error filter, using firing statistics, accounts for unresolved superpositions and other decomposition errors. Using a standard TECA single-fiber needle electrode, signal recorded during isometric, constant, or slow force-varying contractions of up to 50% of the maximal voluntary contraction level, have been successfully analyzed     

A Procedure for Decomposing the Myoelectric Signal Into Its Constituent Action Potentials?Part I: Technique, Theory, and Implementation

A technique has been developed which enables the decomposition (separation) of a myoelectric signal into its constituent motor unit action potential trains. It consists of a multichannel (via one electrode) myoelectric signal recording procedure, a data compression algorithm, a digital filtering algorithm, and a hybrid visual-computer decomposition scheme. The algorithms have been implemented on a PDP 11/34 computer. Of the four major segments of the technique, the decomposition scheme is by far the most involved. The decomposition algorithm uses a-sophisticated template matching routine and details of the firing statistics of the motor units to identify motor unit action potentials in the myoelectric signal, even when they are super-imposed with other motor unit action potentials. In general, the algorithms of the decomposition scheme do not run automatically. They require input from the human operator to maintain reliability and accuracy during a decomposition.     

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.     

Boundary element analysis of the directional sensitivity of theconcentric EMG electrode

Employing the boundary element method, the authors improve earlier models of the concentric electromyography (EMG) electrode by including an accurate geometric representation of the electrode, as well as the mutual electrical influence between the electrode surfaces. A three-dimensional sensitivity function is defined from which information about the preferential direction of sensitivity, blind spots, phase changes, rate of attenuation, and range of pick-up radius can be derived. The study focuses on the intrinsic features linked to the geometry of the electrode. The results show that the cannula perturbs the potential distribution significantly. The preferential directions of sensitivity are determined by the amount of geometric offset between the individual sensitivity functions of the core and the cannula. The sensitivity function also reveals a complicated pattern of phase changes in the pick-up range. Rotation of the electrode about its axis was found to alter the duration, the peak-to-peak amplitude, and the risetime of waveforms recorded from a moving dipole     

Measurements of the Uptake Area of Small-Size Electromyographic Electrodes

Extracellular action potentials from 76 different muscle fibers in the human brachial biceps were recorded, with a 14 lead multielectrode, each leading-off surface being 25 ?m in diameter. Volume conduction of these action potentials was calculated by representing the low-pass filter characteristics of the muscle tissue by a transfer function with one time constant and an attenuation factor. The radial decline of the action potentials was calculated in steps of 5 ?m, and the pickup radius of the electrode, defined as the distance at which the peak-to-peak amplitude of the action potentials declines to 200 ?V, was computed. The pickup radius for the 25 ?m diameter electrode, assuming an average action potential peak-to-peak amplitude of 6.2 mV was 292 Mm. With this uptake area, a fiber density in the brachial biceps of 1.37 fibers/uptake area (the average number of fibers belonging to one motor unit and included within the electrode uptake area) and a fiber radius of 28 ?m, the electrode "sees" 34 different motor units.     

Noninvasive estimation of motor unit conduction velocity distribution using linear electrode arrays

Determining the conduction velocity of motor unit action potentials is one of the most important problems in surface electromyography. The estimate of one average conduction velocity value depends on a variety of uncontrollable factors. More meaningful information is obtained from the estimation of the distribution of the different delays in the myoelectric signals. A solution to the problem is the separation and characterization of the individual components propagating at different velocities. A technique, based on surface electrode array recording, is proposed to estimate motor unit conduction velocity distribution. The method consists in the identification of the single action potentials in the time scale domain (with the continuous wavelet transform) and in the estimation of their conduction velocities based on the beamforming algorithm. The performances of the technique have been evaluated using simulated and real myoelectric signals. The results demonstrate that the technique is accurate and reliable. The method may be useful for the diagnosis of neuromuscular disorders, for the monitoring of muscle fatigue and for noninvasive investigation of individual motor units.     

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.     

A new 3-D finite-element model based on thin-film approximation for microelectrode array recording of extracellular action potential.

A transient finite-element model has been developed to simulate an extracellular action potential recording in a tissue slice by a planar microelectrode array. The thin-film approximation of the active neuron membrane allows the simulation within single finite-element software of the intracellular and extracellular potential fields. In comparison with a compartmental neuron model, it is shown that the thin-film approximation-based model is able to properly represent the neuron bioelectrical behavior in terms of transmembrane current and potential. Moreover, the model is able to simulate extracellular action potential recordings with properties similar to those observed in biological experiments. It is demonstrated that an ideal measurement system model can be used to represent the recording microelectrode, provided that the electronic recording system adapts to the electrode-tissue interface impedance. By comparing it with a point source approximated neuron, it is also shown that the neuron three-dimensional volume should be taken into account to simulate the extracellular action potential recording. Finally, the influence of the electrode size on the signal amplitude is evaluated. This parameter, together with the microelectrode noise, should be taken into account in order to optimize future microelectrode designs in terms of the signal-to-noise ratio.     

Sensitivity and selectivity of intraneural stimulation using a silicon electrode array

Artificial electrical stimulation of peripheral nerves needs the development of multielectrode devices which stimulate individual fibers or small groups in a selective and sensitive way. To this end, a multielectrode array in silicon technology has been developed, as well as experimental paradigms and model calculations for sensitivity and selectivity measures. The array consists of twelve platinum electrode sites (10 x 50 microns at 50 microns interdistance) on a 45 microns thick tip-shaped silicon substrate and a Si3N4 insulating glass cover layer. The tip is inserted in the peroneal nerve of the rat during acute experiments to stimulate alpha motor fibers of the extensor digitorum longus muscle. Sensitivity calculations and experiments show a cubic dependence of the number of stimulated motor units on current amplitude of the stimulatory pulse (recruitment curves), starting at single motor level. Selectivity was tested by a method based on the refractory properties of neurons. At the lowest stimulus levels (for one motor unit) selectivity is maximal when two electrodes are separated by 200-250 microns, which was estimated also on theoretical grounds. The study provides clues for future designs of two- and three-dimensional devices.     

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.     

Recording from a Single Motor Unit During Strong Effort

During strong voluntary effort it is rarely possible to identify the action potentials from single motor units. In large muscles the most selective recordings are obtained with bipolar wire electrodes. To elucidate this experimental finding we have calculated the extracellular field around a single muscle fiber from an intracellular muscle action potential. This model showed that the selectivity of a bipolar electrode is high provided: i) the diameter of the recording surfaces is less than half the diameter of the muscle fibers; ii) the center distance between the recording surfaces is of the same order or smaller than the diameter of the muscle fibers, and when iii) the center-line between the recording surfaces is oriented perpendicular to the direction of the muscle fibers.     

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.     

Evaluation of MUAP shape irregularity-a new concept ofquantification

A coefficient for quantifying the shape irregularities of the motor unit action potential (MUAP) is introduced. This coefficient is defined as the “length” of action potential curve normalized by the signal's amplitude in such a way that it is independent on duration and amplitude. It characterizes only the MUAP shape. The irregularity coefficient may be used to measure the deviations of the potential from the normal MUAP. The properties of this coefficient and its relation to the conventional parameters describing MUAP shape, viz. The number of phases and turns is discussed. The examples of classification of real signals according to this coefficient are presented     

Simulation of intramuscular EMG signals detected using implantable myoelectric sensors (IMES)

The purpose of this study was to test the feasibility of recording independent electromyographic (EMG) signals from the forearm using implantable myoelectric sensors (IMES), for myoelectric prosthetic control. Action potentials were simulated using two different volume conductor models: a finite-element (FE) model that was used to explore the influence of the electrical properties of the surrounding inhomogeneous tissues and an analytical infinite volume conductor model that was used to estimate the approximate detection volume of the implanted sensors. Action potential amplitude increased progressively as conducting electrodes, the ceramic electrode casing and high resistivity encapsulation tissue were added to the model. For the muscle fiber locations examined, the mean increase in EMG root mean square amplitude when the full range of material properties was included in the model was 18.2% (+/-8.1%). Changing the orientation of the electrode with respect to the fiber direction altered the shape of the electrode detection volume and reduced the electrode selectivity. The estimated detection radius of the IMES electrode, assuming a cylindrical muscle cross section, was 4.8, 6.2, and 7.5 mm for electrode orientations of 0 degree, 22.5 degrees, and 45 degrees with respect to the muscle fiber direction.     

A Procedure for Decomposing the Myoelectric Signal Into Its Constituent Action Potentials-Part II: Execution and Test for Accuracy

The scheme for decomposing a myoelectric signal into its constituent motor unit action potential trains described in the paper [3] requires interaction from the human operator. In this paper, guidelines to be employed by the operator in assisting the computerized algorithms in identifying (classifying) a motor unit action potential are presented. The accuracy of the decomposition scheme was evaluated by decomposing a mathematically synthesized myoelectric signal. This signal was constructed by linearly superimposing eight mathematically generated motor unit action potential trains along with Gaussian noise. A skilled operator was able to decompose this signal with an accuracy of 99.8 percent, incurring one error in a total of 435 classifications. The decomposition reproducibility was evaluated by having two experienced operators independently decompose the same record of empirically obtained myoelectric signal. Their results were in total agreement for 479 motor unit action potential classifications belonging to five motor unit action potential trains. Up to eight motor unit action potential trains have been decomposed from one myoelectric signal.     

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.     

Simulation of surface EMG signals for a multilayer volume conductor with a superficial bone or blood vessel

This study analytically describes surface electromyogram (EMG) signals generated by a planar multilayer volume conductor constituted by different subdomains modeling muscle, bone (or blood vessel), fat, and skin tissues. The bone is cylindrical in shape, with a semicircular section. The flat portion of the boundary of the bone subdomain is interfaced with the fat layer tissue, the remaining part of the boundary is in contact with the muscle layer. The volume conductor is a model of physiological tissues in which the bone is superficial, as in the case of the tibia bone, backbone, and bones of the forearm. The muscle fibers are considered parallel to the axes of the bone, so that the model is space invariant in the direction of propagation of the action potential. The proposed model, being analytical, allows faster simulations of surface EMG with respect to previously developed models including bone or blood vessels based on the finite-element method. Surface EMG signals are studied by simulating a library of single-fiber action potentials (SFAP) of fibers in different locations within the muscle domain, simulating the generation, propagation, and extinction of the action potential. The decay of the amplitude of the SFAPs in the direction transversal to the fibers is assessed. The decay in the direction of the bone has a lower rate with respect to the opposite direction. Similar results are obtained by simulating motor unit action potentials (MUAPs) constituted by 100 fibers with territory 5 mm2. M waves and interference EMG signals are also simulated based on the library of SFAPs. Again, the decay of the amplitude of the simulated interference EMG signals is lower approaching the bone with respect to going farther from it. The findings of this study indicate the effect of a superficial bone in enhancing the EMG signals in the transversal direction with respect to the fibers of the considered muscle. This increases the effect of crosstalk. The same mathematical method used to simulate a superficial bone can be applied to simulate other physiological tissues. For example, superficial blood vessels (e.g., basilic vein, brachial artery) can influence the recorded EMG signals. As the electrical conductivity of blood is high (it is of the same order as the longitudinal conductivity in the muscle), the effect on EMG signals is opposite compared to the effect of a superficial bone.