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Even under isometric conditions, muscle contractions are associated with some degree of fiber shortening. The effects of muscle shortening on extracellular electromyographic potentials have not been characterized in detail. Moreover, the anatomical, biophysical, and detection factors influencing the muscle-shortening effects have been neither identified nor understood completely. Herein, we investigated the effects of muscle shortening on the amplitude and duration characteristics of single-fiber, motor unit, and compound muscle action potentials. We found that, at the single-fiber level, two main factors influenced the muscle-shortening effects: (1) the electrode position and distance relative to the myotendinous zone and (2) the electrode distance to the maxima of the dipole field arising from the stationary dipole created at the fiber-tendon junction. Besides, at the motor unit and muscle level, two additional factors were involved: (3) the overlapping between the propagating component of some fibers with the non-propagating component of other fibers and (4) the spatial spreading of the fiber-tendon junctions. The muscle-shortening effects depend critically on the electrode longitudinal distance to the myotendinous zone. When the electrode was placed far from the myotendinous zone, muscle shortening resulted in an enlargement and narrowing of the final (negative) phase of the potential, and this enlargement became less pronounced as the electrode approached the fiber endings. For electrode locations close to the myotendinous zone, muscle shortening caused a depression of both the main (positive) and final (negative) phases of the potential. Beyond the myotendinous zone, muscle shortening led to a decrease of the final (positive) phase. The present results provide reference information that will help to identify changes in MUPs and M waves due to muscle shortening, and thus to differentiate these changes from those caused by muscle fatigue., (© 2021. The Author(s).)

Removing artifacts from nearby motor units is one of the main objectives when processing scanning-EMG recordings. Methods such as median filtering or masked least-squares smoothing (MLSS) can be used to eliminate artifacts in recordings with just one discharge of the motor unit potential (MUP) at each location. However, more effective artifact removal can be achieved if several discharges per position are recorded. In this case, processing usually involves averaging the discharges available at each position and then applying a median filter in the spatial dimension. The main drawback of this approach is that the median filter tends to distort the signal waveform. In this paper, we present a new algorithm that operates on multiple discharges simultaneously and in the spatial dimension. We refer to this algorithm as the multi-masked least-squares smoothing (MMLSS) algorithm: an extension of the MLSS algorithm for the case of multiple discharges. The algorithm is tested using simulated scanning-EMG signals in different recording conditions, i.e., at different levels of muscle contraction and for different numbers of discharges per position. The results demonstrate that the algorithm eliminates artifacts more effectively than any previously available method and does so without distorting the waveform of the signal. Graphical abstract The raw scanning-EMG signal, which can be composed by several discharges of the MU, is processed by the MMLSS algorithm so as to eliminate the artifact interference. Firstly, artifacts are detected for each discharge from the raw signal, obtaining a multi-discharge validity mask that indicates the samples that have been corrupted by artifacts. Secondly, a least-squares smoothing procedure simultaneously operating in the spatial dimension and among the discharges is applied to the raw signal. This second step is performed using only the not contaminated samples according to the validity mask. The resulting MMLSS-processed scanning-EMG signal is clean of artifact interference.

We present a new, automatic, correlation-based method for measuring the duration of motor unit action potentials (MUAPs). The method seeks to replicate the way an expert elctromyographer uses his or her eyes, calculating the start and end of the MUAP waveform on the basis of the degree of similarity of non-excluded discharges. We analysed 68 potentials from normal deltoid muscles during slight contraction. For each MUAP, two experienced electromyographers manually determined start and end marker positions, which were used as gold standard duration positions (GSP) in our subsequent tests. The novel method was compared with Nandedkar's method and a wavelet transform-based method. To compare the three methods, the differences between the automatic marker positions and GSPs were statistically evaluated using one-factor ANOVA, the estimated mean square error, and a Chi-square test on the numbers of automatic marker placements with gross errors. All these parameters showed smaller values for the novel method and in most of the cases were statistically significant. In addition, the parameters of the new method were subjected to a sensitivity study, showing its good performance within a range of clinically useful parameter values. The new automatic method determined start and end markers in a more accurate and reliable manner than both of the acknowledged state-of-the art methods used in our comparison study. Graphical abstract The description of a new automatic duration measurement algorithm based on the similarity among discharges of the same MUAP. This method gave better results than the Nandedkar method and a highly regarded wavelet-based method. The new correlation-based method also had the lowest rate of gross aberrant errors in automatic placements.

Inter-discharge interval distribution modeling of the motor unit firing pattern plays an important role in electromyographic decomposition and the statistical analysis of firing patterns. When modeling firing patterns obtained from automatic procedures, false positives and false negatives can be taken into account to enhance performance in estimating firing pattern statistics. Available models of this type, however, are only approximate and use Gaussian distributions, which are not strictly suitable for modeling renewal point processes. In this paper, the theory of point processes is used to derive an exact solution to the distribution when a gamma distribution is used to model the physiological firing pattern. Besides being exact, the solution provides a way to model the skewness of the inter-discharge distribution, and this may make it possible to obtain a better fit with available experimental data. In order to demonstrate potential applications of the model, we use it to obtain a maximum likelihood estimator of firing pattern statistics. Our tests found this estimator to be reliable over a wide range of firing conditions, whether dealing with real or simulated firing patterns, the proposed solution had better agreement than other models. Graphical Abstract Model of the MU firing pattern generation and detection: f T,1 (τ), IDI PDF of the physiological firing pattern; f T (τ), IDI PDF after modeling undetected firings (false negatives); f S (τ), IDI PDF after modeling classification errors (false positives).

Scanning-EMG is an electrophysiological technique in which the electrical activity of the motor unit is recorded at multiple points along a corridor crossing the motor unit territory. Correct analysis of the scanning-EMG signal requires prior elimination of interference from nearby motor units. Although the traditional processing based on the median filtering is effective in removing such interference, it distorts the physiological waveform of the scanning-EMG signal. In this study, we describe a new scanning-EMG signal processing algorithm that preserves the physiological signal waveform while effectively removing interference from other motor units. To obtain a cleaned-up version of the scanning signal, the masked least-squares smoothing (MLSS) algorithm recalculates and replaces each sample value of the signal using a least-squares smoothing in the spatial dimension, taking into account the information of only those samples that are not contaminated with activity of other motor units. The performance of the new algorithm with simulated scanning-EMG signals is studied and compared with the performance of the median algorithm and tested with real scanning signals. Results show that the MLSS algorithm distorts the waveform of the scanning-EMG signal much less than the median algorithm (approximately 3.5 dB gain), being at the same time very effective at removing interference components. Graphical Abstract The raw scanning-EMG signal (left figure) is processed by the MLSS algorithm in order to remove the artifact interference. Firstly, artifacts are detected from the raw signal, obtaining a validity mask (central figure) that determines the samples that have been contaminated by artifacts. Secondly, a least-squares smoothing procedure in the spatial dimension is applied to the raw signal using the not contaminated samples according to the validity mask. The resulting MLSS-processed scanning-EMG signal (right figure) is clean of artifact interference.

Attention of the investigators is usually pointed to the peak-to-peak characteristics of single-fiber action potentials (SFAPs) that are mainly determined by the depolarizing phase of the intracellular action potential (IAP). However, the final portion of the SFAP has often specific shape that has to be related to peculiarities of the repolarization phase of IAP and the duration of its spike. A novel piecewise SFAP model is proposed to achieve greater insight into the nature of declining portion of the negative phase and of the third phase of SFAP. It was found that the SFAP third phase is essentially determined by the specific profile of the transition of the IAP falling phase toward the resting voltage, whereas the SFAP declining negative phase is more dependent upon the width of the corresponding IAP spike. We tentatively suggest that the duration of the spike of human IAPs should be over approximately 0.75 ms.

The aim of this study was to develop a muscle architecture model able to account for the observed distributions of innervation ratios and fiber densities of different types of motor units in a muscle. A model algorithm is proposed and mathematically analyzed in order to obtain an inverse procedure that allows, by modification of input parameters, control over the output distributions of motor unit fiber densities. The model's performance was tested with independent data from a glycogen depletion study of the medial gastrocnemius of the rat. Results show that the model accurately reproduces the observed physiological distributions of innervation ratios and fiber densities and their relationships. The reliability and accuracy of the new muscle architecture model developed here can provide more accurate models for the simulation of different electromyographic signals.

We present a statistical evaluation and comparison of the simulation outcomes of nine different motor unit architecture modeling approaches, which derive from combinations of four motor unit territory placement algorithms with two innervation pattern algorithms (one of the combinations allows for a double approach). We test how well the outcomes of these models agree with well-established physiological principles. Our results show that algorithms based on independent and uniformly distributed territory placement always lead to an unwanted edge effect consisting in a decay of the number of overlapping motor unit territories toward the edge of the muscle cross section. This edge effect interferes with the properties of the simulated motor units. On the other hand, controlled placement of motor units, so as to minimize the spatial variance of muscle fiber density (MFD), can result in simulated motor units which better reflect the empirical evidence currently available about motor unit architecture.