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

Adaptive EMG decomposition in dynamic conditions based on online learning metrics with tunable hyperparameters

Objective. Developing neural decoders robust to non-stationary conditions is essential to ensure their long-term accuracy and stability. This is particularly important when decoding the neural drive to muscles during dynamic contractions, which pose significant challenges for stationary decoders.Approach. We propose a novel adaptive electromyography (EMG) decomposition algorithm that builds on blind source separation methods by leveraging the Kullback-Leibler divergence and kurtosis of the signals as metrics for online learning. The proposed approach provides a theoretical framework to tune the adaptation hyperparameters and compensate for non-stationarities in the mixing matrix, such as due to dynamic contractions, and to identify the underlying motor neuron (MN) discharges. The adaptation is performed in real-time (∼22 ms of computational time per 100 ms batches).Main results. The hyperparameters of the proposed adaptation captured anatomical differences between recording locations (forearm vs wrist) and generalised across subjects. Once optimised, the proposed adaptation algorithm significantly improved all decomposition performance metrics with respect to the absence of adaptation in a wide range of motion of the wrist (80∘). The rate of agreement, sensitivity, and precision were⩾90%in⩾80%of the cases in both simulated and experimentally recorded data, according to a two-source validation approach.Significance. The findings demonstrate the suitability of the proposed online learning metrics and hyperparameter optimisation to compensate the induced modulation and accurately decode MN discharges in dynamic conditions. Moreover, the study proposes an experimental validation method for EMG decomposition in dynamic tasks.

A fast gradient convolution kernel compensation method for surface electromyogram decomposition

Decomposition of EMG signals provides the decoding of motor unit (MU) discharge timings. In this study, we propose a fast gradient convolution kernel compensation (fgCKC) decomposition algorithm for high-density surface EMG decomposition and apply it to an offline and real-time estimation of MU spike trains. We modified the calculation of the cross-correlation vectors to improve the calculation efficiency of the gradient convolution kernel compensation (gCKC) algorithm. Specifically, the new fgCKC algorithm considers the past gradient in addition to the current gradient. Furthermore, the EMG signals are divided by sliding windows to simulate real-time decomposition, and the proposed algorithm was validated on simulated and experimental signals. In the offline decomposition, fgCKC has the same robustness as gCKC, with sensitivity differences of 2.6 ± 1.3 % averaged across all trials and subjects. Nevertheless, depending on the number of MUs and the signal-to-noise ratio of signals, fgCKC is approximately 3 times faster than gCKC. In the real-time part, the processing only needed 240 ms average per window of EMG signals on a regular personal computer (IIntel(R) Core(TM) i5-12490F 3 GHz, 16 GB memory). These results indicate that fgCKC achieves real-time decomposition by significantly reducing processing time, providing more possibilities for non-invasive neuronal behavior research.

Tutorial: Analysis of central and peripheral motor unit properties from decomposed High-Density surface EMG signals with openhdemg

High-Density surface Electromyography (HD-sEMG) is the most established technique for the non-invasive analysis of single motor unit (MU) activity in humans. It provides the possibility to study the central properties (e.g., discharge rate) of large populations of MUs by analysis of their firing pattern. Additionally, by spike-triggered averaging, peripheral properties such as MUs conduction velocity can be estimated over adjacent regions of the muscles and single MUs can be tracked across different recording sessions. In this tutorial, we guide the reader through the investigation of MUs properties from decomposed HD-sEMG recordings by providing both the theoretical knowledge and practical tools necessary to perform the analyses. The practical application of this tutorial is based on openhdemg, a free and open-source community-based framework for the automated analysis of MUs properties built on Python 3 and composed of different modules for HD-sEMG data handling, visualisation, editing, and analysis. openhdemg is interfaceable with most of the available recording software, equipment or decomposition techniques, and all the built-in functions are easily adaptable to different experimental needs. The framework also includes a graphical user interface which enables users with limited coding skills to perform a robust and reliable analysis of MUs properties without coding.

A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model

Motion predictions for limbs can be performed using commonly called Hill-based muscle models. For this type of models, a surface electromyogram (sEMG) of the muscle serves as an input signal for the activation of the muscle model. However, the Hill model needs additional information about the mechanical system state of the muscle (current length, velocity, etc.) for a reliable prediction of the muscle force generation and, hence, the prediction of the joint motion. One feature that contains potential information about the state of the muscle is the position of the center of the innervation zone. This feature can be further extracted from the sEMG. To find the center, a wavelet-based algorithm is proposed that localizes motor unit potentials in the individual channels of a single-column sEMG array and then identifies innervation point candidates. In the final step, these innervation point candidates are clustered in a density-based manner. The center of the largest cluster is the estimated center of the innervation zone. The algorithm has been tested in a simulation. For this purpose, an sEMG simulator was developed and implemented that can compute large motor units (1,000's of muscle fibers) quickly (within seconds on a standard PC).

Advances in EMG measurement techniques, analysis procedures, and the impact of muscle mechanics on future requirements for the methodology

Muscular coordination enables locomotion and interaction with the environment. For more than 50 years electromyography (EMG) has provided insights into the central nervous system control of individual muscles or muscle groups, enabling both fine and gross motor functions. This information is available either at individual motor units (Mus) level or on a more global level from the coordination of different muscles or muscle groups. In particular, non-invasive EMG methods such as surface EMG (sEMG) or, more recently, spatial mapping methods (High-Density EMG - HDsEMG) have found their place in research into biomechanics, sport and exercise, ergonomics, rehabilitation, diagnostics, and increasingly for the control of technical devices. With further technical advances and a growing understanding of the relationship between EMG and movement task execution, it is expected that with time, especially non-invasive EMG methods will become increasingly important in movement sciences. However, while the total number of publications per year on non-invasive EMG methods is growing exponentially, the number of publications on this topic in journals with a scope in movement sciences has stagnated in the last decade. This review paper contextualizes non-invasive EMG development over the last 50 years, highlighting methodological progress. Changes in research topics related to non-invasive EMG were identified. Today non-invasive EMG procedures are increasingly used to control technical devices, where muscle mechanics have a minor influence. In movement science, however, the effect of muscle mechanics on the EMG signal cannot be neglected. This explains why non-invasive EMG's relevance in movement sciences has not developed as expected.

Non-invasive estimation of muscle fibre size from high-density electromyography

Because of the biophysical relation between muscle fibre diameter and the propagation velocity of action potentials along the muscle fibres, motor unit conduction velocity could be a non-invasive index of muscle fibre size in humans. However, the relation between motor unit conduction velocity and fibre size has been only assessed indirectly in animal models and in human patients with invasive intramuscular EMG recordings, or it has been mathematically derived from computer simulations. By combining advanced non-invasive techniques to record motor unit activity in vivo, i.e. high-density surface EMG, with the gold standard technique for muscle tissue sampling, i.e. muscle biopsy, here we investigated the relation between the conduction velocity of populations of motor units identified from the biceps brachii muscle, and muscle fibre diameter. We demonstrate the possibility of predicting muscle fibre diameter (R2  = 0.66) and cross-sectional area (R2  = 0.65) from conduction velocity estimates with low systematic bias (∼2% and ∼4% respectively) and a relatively low margin of individual error (∼8% and ∼16%, respectively). The proposed neuromuscular interface opens new perspectives in the use of high-density EMG as a non-invasive tool to estimate muscle fibre size without the need of surgical biopsy sampling. The non-invasive nature of high-density surface EMG for the assessment of muscle fibre size may be useful in studies monitoring child development, ageing, space and exercise physiology, although the applicability and validity of the proposed methodology need to be more directly assessed in these specific populations by future studies. KEY POINTS: Because of the biophysical relation between muscle fibre size and the propagation velocity of action potentials along the sarcolemma, motor unit conduction velocity could represent a potential non-invasive candidate for estimating muscle fibre size in vivo. This relation has been previously assessed in animal models and humans with invasive techniques, or it has been mathematically derived from simulations. By combining high-density surface EMG with muscle biopsy, here we explored the relation between the conduction velocity of populations of motor units and muscle fibre size in healthy individuals. Our results confirmed that motor unit conduction velocity can be considered as a novel biomarker of fibre size, which can be adopted to predict muscle fibre diameter and cross-sectional area with low systematic bias and margin of individual error. The proposed neuromuscular interface opens new perspectives in the use of high-density EMG as a non-invasive tool to estimate muscle fibre size without the need of surgical biopsy sampling.

Effects of muscle shortening on single-fiber, motor unit, and compound muscle action potentials

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.

Design and Characterization of a Textile Electrode System for the Detection of High-Density sEMG

Muscle activity monitoring in dynamic conditions is a crucial need in different scenarios, ranging from sport to rehabilitation science and applied physiology. The acquisition of surface electromyographic (sEMG) signals by means of grids of electrodes (High-Density sEMG, HD-sEMG) allows obtaining relevant information on muscle function and recruitment strategies. During dynamic conditions, this possibility demands both a wearable and miniaturized acquisition system and a system of electrodes easy to wear, assuring a stable electrode-skin interface. While recent advancements have been made on the former issue, detection systems specifically designed for dynamic conditions are at best incipient. The aim of this work is to design, characterize, and test a wearable, HD-sEMG detection system based on textile technology. A 32-electrodes, 15 mm inter-electrode distance textile grid was designed and prototyped. The electrical properties of the material constituting the detection system and of the electrode-skin interface were characterized. The quality of sEMG signals was assessed in both static and dynamic contractions. The performance of the textile detection system was comparable to that of conventional systems in terms of stability of the traces, properties of the electrode-skin interface and quality of the collected sEMG signals during quasi-isometric and highly dynamic tasks.

The Effects of Spinal Manipulation on Motor Unit Behavior

Over recent years, a growing body of research has highlighted the neural plastic effects of spinal manipulation on the central nervous system. Recently, it has been shown that spinal manipulation improved outcomes, such as maximum voluntary force and limb joint position sense, reflecting improved sensorimotor integration and processing. This study aimed to further evaluate how spinal manipulation can alter neuromuscular activity. High density electromyography (HD sEMG) signals from the tibialis anterior were recorded and decomposed in order to study motor unit changes in 14 subjects following spinal manipulation or a passive movement control session in a crossover study design. Participants were asked to produce ankle dorsiflexion at two force levels, 5% and 10% of maximum voluntary contraction (MVC), following two different patterns of force production (“ramp” and “ramp and maintain”). A significant decrease in the conduction velocity (p = 0.01) was observed during the “ramp and maintain” condition at 5% MVC after spinal manipulation. A decrease in conduction velocity suggests that spinal manipulation alters motor unit recruitment patterns with an increased recruitment of lower threshold, lower twitch torque motor units.

Model-Based Sensitivity Analysis of EMG Clustering Index With Respect to Motor Unit Properties: Investigating Post-Stroke FDI Muscle

The objective of this study is to explore the diagnostic decision and sensitivity of the surface electromyogram (EMG) clustering index (CI) with respect to post-stroke motor unit (MU) alterations through a simulation approach by the existing motor neuron pool model and surface EMG model. In the simulation analysis, three patterns of diagnostic decisions were presented in 24 groups representing eight types in three degrees of MU alterations. Specifically, the CI decision exhibited an abnormally increased pattern for five types, an abnormally decreased pattern for two types, and an invariant pattern for one type. Furthermore, the CI diagnostic decision was found to be highly sensitive to three types because a 50% degree of alteration in these types resulted in a distinct deviation of 2.5 in the CI Z-score. The mixed CI patterns were confirmed in experimental data collected from the paretic muscles of 14 subjects with stroke, as compared to the healthy muscles of 10 control subjects. Given the simulation results as a guideline, the CI diagnostic decision could be interpreted from general neural or muscular changes into specific MU changes (in eight types). This can further promote clinical applications of the convenient surface EMG tool in examining and monitoring paretic muscle changes toward customized stroke rehabilitation.

Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges-A Systematic Review

Background: In order to reduce the risk of work-related musculoskeletal disorders (WMSDs) several methods have been developed, accepted by the international literature and used in the workplace. The purpose of this systematic review was to describe recent implementations of wearable sensors for quantitative instrumental-based biomechanical risk assessments in prevention of WMSDs. Methods: Articles written until 7 May 2018 were selected from PubMed, Scopus, Google Scholar and Web of Science using specific keywords. Results: Instrumental approaches based on inertial measurement units and sEMG sensors have been used for direct evaluations to classify lifting tasks into low and high risk categories. Wearable sensors have also been used for direct instrumental evaluations in handling of low loads at high frequency activities by using the local myoelectric manifestation of muscle fatigue estimation. In the field of the rating of standard methods, on-body wireless sensors network-based approaches for real-time ergonomic assessment in industrial manufacturing have been proposed. Conclusions: Few studies foresee the use of wearable technologies for biomechanical risk assessment although the requirement to obtain increasingly quantitative evaluations, the recent miniaturization process and the need to follow a constantly evolving manual handling scenario is prompting their use.

Surface electromyography (sEMG) of extradiaphragm respiratory muscles in healthy subjects: A systematic review

The aim of this systematic review was to examine procedures used and outcome measures reported from surface EMG (sEMG) of extradiaphragm inspiratory muscles in healthy people. Relevant articles were searched using the concepts "electromyography (EMG)", "respiratory muscles (sternocleidomastoid [SM], scalene, intercostal [IC] and parasternal)" and "healthy" in the electronic databases: MEDLINE, PubMed, EMBASE, Cochrane CENTRAL and Database of Systematic Reviews, CINAHL, SPORTDiscus, LILACS, and PEDro. Twenty-five papers were included and quality assessment was performed using an adapted Downs and Black checklist. Twenty-eight percent of included papers were classified as moderate quality and the rest were low quality. The SM was the muscle most often investigated. Description of EMG techniques were often incomplete for features such as the procedure before electrode placement, description of the surface electrodes, the EMG detection mode and amplification. Of note, descriptions of the IC muscle electrode positioning varied widely. Comparison of outcomes among studies was challenging because of the very diverse EMG outcomes reported. There are many controversies regarding methods and technique used to assess sEMG of extradiaphragm inspiratory muscles. Therefore, studies with higher methodological quality utilizing standardized EMG procedures including electrode positioning will enable accurate and reliable comparison among studies of the extradiaphragm inspiratory muscles.

Myoelectric manifestation of muscle fatigue in repetitive work detected by means of miniaturized sEMG sensors

Upper limb work-related musculoskeletal disorders have a 12-month prevalence ranging from 12 to 41% worldwide and can be partly caused by handling low loads at high frequency. The association between the myoelectric manifestation of elbow flexor muscle fatigue and occupational physical demand has never been investigated. It was hypothesized that an elbow flexor muscle fatigue index could be a valid risk indicator in handling low loads at high frequency. This study aims to measure the myoelectric manifestation of muscle fatigue of the three elbow flexor muscles during the execution of the work tasks in different risk conditions. Fifteen right-handed healthy adults were screened using a movement analysis laboratory consisting of optoelectronic, dynamometer and surface electromyographic systems. The main result indicates that the fatigue index calculated from the brachioradialis is sensitive to the interaction among risk classes, session and gender, and above all it is sensitive to the risk classes.

A new approach for multi-channel surface EMG signal simulation

Simulation models are necessary for testing the performance of newly developed approaches before they can be applied to interpreting experimental data, especially when biomedical signals such as surface electromyogram (SEMG) signals are involved. A new and easily implementable surface EMG simulation model was developed in this study to simulate multi-channel SEMG signals. A single fiber action potential (SFAP) is represented by the sum of three Gaussian functions. SFAP waveforms can be modified by adjusting the amplitude and bandwidth of the Gaussian functions. SEMG signals were successfully simulated at different detected locations. Effects of the fiber depth, electrode position and conduction velocity of SFAP on motor unit action potential (MUAP) were illustrated. Results demonstrate that the easily implementable SEMG simulation approach developed in this study can be used to effectively simulate SEMG signals.

A Muscle Fibre Conduction Velocity Tracking ASIC for Local Fatigue Monitoring

Electromyography analysis can provide information about a muscle's fatigue state by estimating Muscle Fibre Conduction Velocity (MFCV), a measure of the travelling speed of Motor Unit Action Potentials (MUAPs) in muscle tissue. MFCV better represents the physical manifestations of muscle fatigue, compared to the progressive compression of the myoelectic Power Spectral Density, hence it is more suitable for a muscle fatigue tracking system. This paper presents a novel algorithm for the estimation of MFCV using single threshold bit-stream conversion and a dedicated application-specified integrated circuit (ASIC) for its implementation, suitable for a compact, wearable and easy to use muscle fatigue monitor. The presented ASIC is implemented in a commercially available AMS 0.35 [Formula: see text] CMOS technology and utilizes a bit-stream cross-correlator that estimates the conduction velocity of the myoelectric signal in real time. A test group of 20 subjects was used to evaluate the performance of the developed ASIC, achieving good accuracy with an error of only 3.2% compared to Matlab.

Towards Real-Time Estimation of Muscle-Fiber Conduction Velocity Using Delay-Locked Loop

Decrease in muscle-fiber conduction velocity (MFCV) during sustained contraction has been widely accepted as myoelectric manifestation of muscle fatigue. Several methods have been proposed in the literature for MFCV estimation by analysing surface electromyography (EMG), e.g., cross-correlation (CC) function and maximum likelihood (ML). However, for all the availablemethods, windowing of the EMG signal and computationally demanding calculations are required, limiting the possibility to continuously monitor muscle fatigue in real time. In the present study, an adaptive scheme is proposed that permits real-time estimation of MFCV. The proposed scheme is based on a delay-lockedloop (DLL). Asecond-orderloop is adopted to track the delay variationover time. An error filter is employed to approximate a ML estimation in case of colored noise. Furthermore, the DLL system is extended for multichannel CV estimation. The performance of the proposed method is evaluated by both dedicated simulations and real EMG signals. Our results show the accuracy of the proposed method to be comparable to that of theML method formuch lower (1/40) computational complexity, especially suited for real-time MFCV measurements. Use of this method can enable new studies onmyoelectric fatigue, possibly leading to new insight on the underlying physiological processes.

The Assessment of Stent Effectiveness Using a Wearable Beamforming MEMS Microphone Array System

Studies involving turbulent flow have been carried out in many parts of the cardiovascular system, and it has been widely reported that turbulence related to stenosis (narrowing) of arteries creates audible sounds, which may be analyzed to yield information about the nature and severity of the blockage. Results so far indicate that the high frequency content of the sounds generally increases with the degree of stenosis. In this paper, we designed and built an MEMs microphone array and a signal acquisition board to improve the detection of coronary occlusions using an approach based on the recording and analysis of isolated diastolic heart sounds associated with turbulent blood flow in occluded coronary arteries. The nonlinear dynamic analysis method based on approximate entropy has been proposed for the analysis of diastolic heart sounds from patients with single coronary occlusions, before and after stent placement procedures. The nonlinear dynamic analysis (approximate entropy) measures of the diastolic heart sounds recorded from eight patients with single coronary occlusions and two normal subjects were estimated. In addition, a spectral analysis based on the fast Fourier transform was used to estimate the energy content of the recorded signals. Results suggest the presence of high nonlinear (approximate entropy) values of diastolic heart sounds associated with coronary artery disease (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$p<0.01$ \end{document}) as well as significant differences in the energy content of the heart sound signals above and below 150 Hz (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$p<0.05$ \end{document}).

Influence of timing variability between motor unit potentials on M-wave characteristics

The transient enlargement of the compound muscle action potential (M wave) after a conditioning contraction is referred to as potentiation. It has been recently shown that the potentiation of the first and second phases of a monopolar M wave differed drastically; namely, the first phase remained largely unchanged, whereas the second phase underwent a marked enlargement and shortening. This dissimilar potentiation of the first and second phases has been suggested to be attributed to a transient increase in conduction velocity after the contraction. Here, we present a series of simulations to test if changes in the timing variability between motor unit potentials (MUPs) can be responsible for the unequal potentiation (and shortening) of the first and the second M-wave phases. We found that an increase in the mean motor unit conduction velocity resulted in a marked enlargement and narrowing of both the first and second M-wave phases. The enlargement of the first phase caused by a global increase in motor unit conduction velocities was apparent even for the electrode located over the innervation zone and became more pronounced with increasing distance to the innervation zone, whereas the potentiation of the second phase was largely independent of electrode position. Our simulations indicate that it is unlikely that an increase in motor unit conduction velocities (accompanied or not by changes in their distribution) could account for the experimental observation that only the second phase of a monopolar M wave, but not the first, is enlarged after a brief contraction. However, the combination of an increase in the motor unit conduction velocities and a spreading of the motor unit activation times could potentially explain the asymmetric potentiation of the M-wave phases.

Higher Neuromuscular Manifestations of Fatigue in Dynamic than Isometric Pull-Up Tasks in Rock Climbers

Neuromuscular assessment of rock climbers has been mainly focused on forearm muscles in the literature. We aimed to extend the body of knowledge investigating on two other upper limb muscles during sport-specific activities in nine male rock climbers. We assessed neuromuscular manifestations of fatigue recording surface electromyographic signals from brachioradialis and teres major muscles, using multi-channel electrode arrays. Participants performed two tasks until volitional exhaustion: a sequence of dynamic pull-ups and an isometric contraction sustaining the body at half-way of a pull-up (with the elbows flexed at 90°). The tasks were performed in randomized order with 10 minutes of rest in between. The normalized rate of change of muscle fiber conduction velocity was calculated as the index of fatigue. The time-to-task failure was significantly shorter in the dynamic (31 ±10 s) than isometric contraction (59 ±19 s). The rate of decrease of muscle fiber conduction velocity was found steeper in the dynamic than isometric task both in brachioradialis (isometric: −0.2 ±0.1%/s; dynamic: −1.2 ±0.6%/s) and teres major muscles (isometric: −0.4±0.3%/s; dynamic: −1.8±0.7%/s). The main finding was that a sequence of dynamic pull-ups lead to higher fatigue than sustaining the body weight in an isometric condition at half-way of a pull-up. Furthermore, we confirmed the possibility to properly record physiological CV estimates from two muscles, which had never been studied before in rock climbing, in highly dynamic contractions.

Motor unit action potential conduction velocity estimated from surface electromyographic signals using image processing techniques

In surface electromyography (surface EMG, or S-EMG), conduction velocity (CV) refers to the velocity at which the motor unit action potentials (MUAPs) propagate along the muscle fibers, during contractions. The CV is related to the type and diameter of the muscle fibers, ion concentration, pH, and firing rate of the motor units (MUs). The CV can be used in the evaluation of contractile properties of MUs, and of muscle fatigue. The most popular methods for CV estimation are those based on maximum likelihood estimation (MLE). This work proposes an algorithm for estimating CV from S-EMG signals, using digital image processing techniques. The proposed approach is demonstrated and evaluated, using both simulated and experimentally-acquired multichannel S-EMG signals. We show that the proposed algorithm is as precise and accurate as the MLE method in typical conditions of noise and CV. The proposed method is not susceptible to errors associated with MUAP propagation direction or inadequate initialization parameters, which are common with the MLE algorithm. Image processing -based approaches may be useful in S-EMG analysis to extract different physiological parameters from multichannel S-EMG signals. Other new methods based on image processing could also be developed to help solving other tasks in EMG analysis, such as estimation of the CV for individual MUs, localization and tracking of innervation zones, and study of MU recruitment strategies.

The differential effect of metabolic alkalosis on maximum force and rate of force development during repeated, high-intensity cycling

The purpose of this investigation was to assess the influence of sodium bicarbonate supplementation on maximal force production, rate of force development (RFD), and muscle recruitment during repeated bouts of high-intensity cycling. Ten male and female ( n = 10) subjects completed two fixed-cadence, high-intensity cycling trials. Each trial consisted of a series of 30-s efforts at 120% peak power output (maximum graded test) that were interspersed with 30-s recovery periods until task failure. Prior to each trial, subjects consumed 0.3 g/kg sodium bicarbonate (ALK) or placebo (PLA). Maximal voluntary contractions were performed immediately after each 30-s effort. Maximal force (Fmax) was calculated as the greatest force recorded over a 25-ms period throughout the entire contraction duration while maximal RFD (RFDmax) was calculated as the greatest 10-ms average slope throughout that same contraction. Fmax declined similarly in both the ALK and PLA conditions, with baseline values (ALK: 1,226 ± 393 N; PLA: 1,222 ± 369 N) declining nearly 295 ± 54 N [95% confidence interval (CI) = 84–508 N; P < 0.006]. RFDmax also declined in both trials; however, a differential effect persisted between the ALK and PLA conditions. A main effect of condition was observed across the performance time period, with RFDmax on average higher during ALK (ALK: 8,729 ± 1,169 N/s; PLA: 7,691 ± 1,526 N/s; mean difference between conditions 1,038 ± 451 N/s, 95% CI = 17–2,059 N/s; P < 0.048). These results demonstrate a differential effect of alkalosis on maximum force vs. maximum rate of force development during a whole body fatiguing task.

Wrist torque estimation during simultaneous and continuously changing movements: surface vs. untargeted intramuscular EMG

In this paper, the predictive capability of surface and untargeted intramuscular electromyography (EMG) was compared with respect to wrist-joint torque to quantify which type of measurement better represents joint torque during multiple degrees-of-freedom (DoF) movements for possible application in prosthetic control. Ten able-bodied subjects participated in the study. Surface and intramuscular EMG was recorded concurrently from the right forearm. The subjects were instructed to track continuous contraction profiles using single and combined DoF in two trials. The association between torque and EMG was assessed using an artificial neural network. Results showed a significant difference between the two types of EMG (P < 0.007) for all performance metrics: coefficient of determination (R(2)), Pearson correlation coefficient (PCC), and root mean square error (RMSE). The performance of surface EMG (R(2) = 0.93 ± 0.03; PCC = 0.98 ± 0.01; RMSE = 8.7 ± 2.1%) was found to be superior compared with intramuscular EMG (R(2) = 0.80 ± 0.07; PCC = 0.93 ± 0.03; RMSE = 14.5 ± 2.9%). The higher values of PCC compared with R(2) indicate that both methods are able to track the torque profile well but have some trouble (particularly intramuscular EMG) in estimating the exact amplitude. The possible cause for the difference, thus the low performance of intramuscular EMG, may be attributed to the very high selectivity of the recordings used in this study.

Estimation of impulse response between electromyogram signals for use in conduction delay distribution estimation

The time delay between two surface electromyograms (EMGs) acquired along the conduction path is used to estimate mean action potential conduction velocity. Modeling the linear impulse response between "upstream" and "downstream" EMG signals permits an estimate of the distribution of velocities, providing more information. In this work, we analyzed EMG from bipolar electrodes placed on the tibialis anterior of 36 subjects, using an inter-electrode distance of 10 mm. Regularized least squares was used to fit the coefficients of a finite impulse response model. We trained the model on one recording, then tested on two others. The optimum correlation between the model-predicted and actual EMG averaged 0.70. We also compared estimation of the mean conduction delay from the peak time of the impulse response to the "gold standard" peak time of the cross-correlation between the upstream and downstream EMG signals. Optimal models differed from the gold standard by 0.02 ms, on average. Model performance was influenced by the regularization parameters. The impulse responses, however, incorrectly contained substantive power at very low time delays, causing delay distribution estimates to exhibit high probabilities at very short conduction delays. Unrealistic distribution estimates resulted. Larger inter-electrode spacing may be required to alleviate this limitation.

Multi-scale surface electromyography modeling to identify changes in neuromuscular activation with myofascial pain

To solve the limitations in using the conventional parametric measures to define myofascial pain, a 3-D multi-scale wavelet energy variation graph is proposed as a way to inspect the pattern of surface electromyography (SEMG) variation between the dominant and nondominant sides at different frequency scales during a muscle contraction cycle and the associated changes with the upper-back myofascial pain. The model was developed based on the property of the wavelet energy of the SEMG signal revealing the degree of correspondence between the shape of the motor unit action potential and the wavelet waveform at a certain scale in terms of the frequency band. The characteristic pattern of the graph for each group (30 normal and 26 patient subjects) was first derived and revealed the dominant-hand effect and the changes with myofascial pain. Through comparison of individual graphs across subjects, we found that the graph pattern reveals a sensitivity of 53.85% at a specificity of 83.33% in the identification of myofascial pain. The changes in these patterns provide insight into the transformation between different fiber recruitment, which cannot be explored using conventional SEMG features. Therefore, this multi-scale analysis model could provide a reliable SEMG features to identify myofascial pain.

Surface electromyography features in manual workers affected by carpal tunnel syndrome

INTRODUCTION

Alterations in surface electromyographic (sEMG) signals of the abductor pollicis brevis muscle were evaluated in 24 non-manual workers and 40 manual workers (25 asymptomatic and 15 reporting CTS symptoms).

METHODS

The initial value (IV) and the normalized rate of change (NRC) of average rectified value (ARV), mean frequency of the power spectrum (MNF), and muscle fiber conduction velocity (MFCV) were calculated during contractions at 20% and 50% of maximal voluntary contraction (MVC). Neuromuscular efficiency (NME) and kurtosis of the sEMG amplitude distribution were estimated.

RESULTS

With respect to controls, manual workers showed higher NME, lower ARV IV, and reduced myoelectric manifestations of fatigue (lower MNF NRC for both contraction levels, and lower MFCV NRC at 50% MVC). Kurtosis at 20% MVC showed higher values in symptomatic manual workers than in the other two groups.

CONCLUSIONS

Kurtosis seems to be a promising parameter for use in monitoring individuals who develop CTS.

A simulation-based analysis of motor unit number index (MUNIX) technique using motoneuron pool and surface electromyogram models

Motor unit number index (MUNIX) measurement has recently achieved increasing attention as a tool to evaluate the progression of motoneuron diseases. In our current study, the sensitivity of the MUNIX technique to changes in motoneuron and muscle properties was explored by a simulation approach utilizing variations on published motoneuron pool and surface electromyogram (EMG) models. Our simulation results indicate that, when keeping motoneuron pool and muscle parameters unchanged and varying the input motor unit numbers to the model, then MUNIX estimates can appropriately characterize changes in motor unit numbers. Such MUNIX estimates are not sensitive to different motor unit recruitment and rate coding strategies used in the model. Furthermore, alterations in motor unit control properties do not have a significant effect on the MUNIX estimates. Neither adjustment of the motor unit recruitment range nor reduction of the motor unit firing rates jeopardizes the MUNIX estimates. The MUNIX estimates closely correlate with the maximum M-wave amplitude. However, if we reduce the amplitude of each motor unit action potential rather than simply reduce motor unit number, then MUNIX estimates substantially underestimate the motor unit numbers in the muscle. These findings suggest that the current MUNIX definition is most suitable for motoneuron diseases that demonstrate secondary evidence of muscle fiber reinnervation. In this regard, when MUNIX is applied, it is of much importance to examine a parallel measurement of motor unit size index (MUSIX), defined as the ratio of the maximum M-wave amplitude to the MUNIX. However, there are potential limitations in the application of the MUNIX methods in atrophied muscle, where it is unclear whether the atrophy is accompanied by loss of motor units or loss of muscle fiber size.

Motor unit properties of biceps brachii during dynamic contractions in chronic stroke patients

The aim of this study was to investigate motor unit (MU) characteristics of the biceps brachii during sinusoidal contractions in chronic stroke patients using high-density surface electromyography. Ten sinusoidal elbow flexion and extension movements were performed both passively and actively by 18 stroke patients and 20 healthy subjects. Motor unit action potentials (MUAPs) were extracted, and their root-mean-square value (RMS(MUAP)) was calculated. RMS(MUAP) was significantly larger in stroke than in healthy subjects. In both groups RMS(MUAP) was smaller during the stretch phase of passive movement than during active movement. The larger MUAPs indicate enlarged MUs, possibly as a result of reinnervation. The lower RMS(MUAP) values during passive stretch than during active movement indicates that the stretch reflex mainly activates smaller MUs, while a larger part of the MU pool can be recruited voluntarily. RMS(MUAP) may have added value for monitoring changes in peripheral MU properties after stroke.

The behavior of action potential conduction velocity on isokinetic knee extension tests

The present study investigates the behavior of action potential conduction velocity (CV) on each repetition of an isokinetic test set and on each set as a whole. A total of seven healthy men (27.7 ± 2.8 yrs, 1.74 ± 0.06 m, and 79.6 ± 11.0 Kg) performed 3 (three) sets of 10 (ten) maximal concentric repetitions of dominant knee extension at 60°/s on an isokinetic dynamometer, with 1 minute of rest interval between the sets. The surface electromyographic (SEMG) signals were recorded from the vastus lateralis muscle during the exercises. CV was estimated with a spectral matching method which requires the SEMG acquisition technique based on a flexible linear array of electrodes (here used with 8 electrodes and 5 mm inter-electrode distance). With the view to minimize the factors other than fatigue that also influence the CV behavior, only the extension phase of the isokinetic exercise repetition was considered for measurements. Results showed that CV usually increases during a single repetition whereas it has a decreasing tendency along the isokinetic set seen as a whole.

A preliminary study of action potential propagation in paretic muscle of stroke survivors

Individual motor unit action potential (MUAP) propagation along the muscle fibers was assessed in paretic muscle of stroke survivors. Single motor unit activity was recorded from the biceps brachii muscle on the paretic side of stroke survivors using a surface electrode array. Among the 15 clearly identified low-threshold motor units, six units showed little delay between MUAPs on different electrodes aligned along muscle fibers, resulting in abnormally high values of calculated propagation velocities, while clear action potential delay was observed for the other motor units. Several factors that possibly lead to this observation are discussed.

Motor unit properties of biceps brachii in chronic stroke patients assessed with high-density surface EMG

The aim of this study was to investigate motor unit (MU) characteristics of the biceps brachii in poststroke patients using high-density surface electromyography (sEMG). Eighteen chronic hemiparetic stroke patients took part. The Fugl-Meyer score for the upper extremity was assessed. Subjects performed an isometric step contraction consisting of force levels from 5%-50% maximal voluntary contraction while sEMG of the biceps brachii was recorded with a two-dimensional 16-channel electrode array. This was repeated for both sides. Motor unit action potentials (MUAPs) were extracted from the EMG signals, and their root-mean-square value (RMS(MUAP), reflecting MU size) and mean frequency of the power spectrum (FMEAN(MUAP), reflecting recruitment threshold) were calculated. FMEAN(MUAP) was smaller on the affected than on the unaffected side, indicating an increased contribution of low-threshold MUs, possibly related to degeneration of high-threshold MUs. The ratio of RMS(MUAP) on the affected side divided by that on the unaffected side correlated significantly with the Fugl-Meyer score. This ratio may reflect the extent to which reinnervation has occurred on the affected side.

A method for better positioning bipolar electrodes for lower limb EMG recordings during dynamic contractions

To obtain a high quality EMG acquisition, the signal must be recorded as far away as possible from muscle innervations and tendon zones, which are known to shift during dynamic contractions. This study describes a methodology, using commercial bipolar electrodes, to identify better electrode positions for superficial EMG of lower limb muscles during dynamic contractions. Eight female volunteers participated in this study. Myoelectric signals of the vastus lateralis, gastrocnemius medialis, peroneus longus and tibialis anterior muscles were acquired during maximum isometric contractions using bipolar electrodes. The electrode positions of each muscle were selected assessing SENIAM and then, other positions were located along the length of muscle up and down the SENIAM site. The raw signal (density), the linear envelopes, the RMS value, the motor point site, the position of the IZ and its shift during dynamic contractions were taken into account to select and compare electrode positions. For vastus lateralis and peroneus longus, the best sites were 66% and 25% of muscle length, respectively (similar to SENIAM location). The position of the tibialis anterior electrodes presented the best signal at 47.5% of its length (different from SENIAM location). The position of the gastrocnemius medialis electrodes was at 38% of its length and SENIAM does not specify a precise location for signal acquisition. The proposed method should be considered as another methodological step in every EMG study to guarantee the quality of the signal and subsequent human movement interpretations.

Estimating motor unit discharge patterns from high-density surface electromyogram

OBJECTIVE

We systematically tested the capability of the Convolution Kernel Compensation (CKC) method to identify motor unit (MU) discharge patterns from the simulated and experimental surface electromyogram (sEMG) during low-force contractions.

METHODS

sEMG was detected with a grid of 13 x 5 electrodes. In simulated signals with 20 dB signal-to-noise ratio, 11+/-3 out of 63 concurrently active MUs were identified with sensitivity >95% in the estimation of their discharge times. In experimental signals recorded at 0-10% of the maximal force, the discharge patterns of (range) 11-19 MUs (abductor pollicis; n=8 subjects), 9-17 MUs (biceps brachii; n=2), 7-11 MUs (upper trapezius; n=2), and 6-10 MUs (vastus lateralis; n=2) were identified. In the abductor digiti minimi muscle of one subject, the decomposition results from concurrently recorded sEMG and intramuscular EMG (iEMG) were compared; the two approaches agreed on 98+/-1% of MU discharges.

CONCLUSION

It is possible to identify the discharge patterns of several MUs during low-force contractions from high-density sEMG.

SIGNIFICANCE

sEMG can be used for the analysis of individual MUs when the application of needles is not desirable or in combination with iEMG to increase the number of sampled MUs.

Technology and instrumentation for detection and conditioning of the surface electromyographic signal: state of the art

The aim of this review is to present the state of the art of the technology of detection and conditioning systems for surface electromyography (sEMG). The first part of the manuscript focuses on the sEMG electrode system technology: the electrode classification, impedance, noise, transfer function, the spatial filtering effect of surface electrode configurations, the effects of electrode geometry, and location on the recorded sEMG signal. Examples of experimental sEMG signals are provided to show the potential value of high-density sEMG electrode grids and multichannel amplifiers that allow to add spatial information to the temporal information content of the sEMG signal. Furthermore, the results of a simple simulation are reported, in order to emphasize the effects of the subcutaneous tissue layers and of the detection volume on the recorded sEMG signal. The second part of the manuscript focuses on the sEMG amplifier technology: the front end amplifier characteristics for signal conditioning, the methods for stimulation artifact reduction, filtering methods, safety requirements, and the methods for analog-to-digital conversion of the sEMG signal.

Optimization of input parameters of an EMG-Force model in constant and sinusoidal force contractions

In an electromyographic and muscle force (EMG-Force) model, the variability and uncertainty of the input muscle parameters increase the difficulty of assessing this type of model. In this study, a Monte Carlo method is used to evaluate the robustness and the sensitivity of an EMG-Force model, recently developed by our team, for two groups of simulations (constant and sinusoidal force contractions). Two existing criteria (EMG/force and force/force-variability relations) and a new criterion derived from this model (Root Mean Square error, Error(RMS), between the force command and the generated force) are used to extract relevant simulations and obtain the optimized parameter ranges in constant force contractions, while only the new criterion could be valuable in sinusoidal force contractions. The comparison of obtained results from the two groups of simulations has shown that the new criterion can replace the two existing criteria in constant and sinusoidal force contractions to give rise to stable optimized input parameter ranges for the studied EMG-Force model.

Analysis of motor units with high-density surface electromyography

Although the behaviour of individual motor units is classically studied with intramuscular EMG, recently developed techniques allow its analysis also from EMG recorded in multiple locations over the skin surface (high-density surface EMG). The analysis of motor units from the surface EMG is useful when the insertion of needles is not desirable or not possible. Moreover, surface EMG allows the measure of motor unit properties which are difficult to assess with invasive technology (e.g., muscle fiber conduction velocity or location of innervation zones) and may increase the number of detectable motor units with respect to selective intramuscular recordings. Although some limitations remain, both the discharge pattern and muscle fiber properties of individual motor units can currently be analyzed non-invasively. This review presents the conditions and methodologies which allow the investigation of motor units with surface EMG.

Detection of individual motor units of the puborectalis muscle by non-invasive EMG electrode arrays

PURPOSE

The purpose of the study was to demonstrate that anatomical features of individual motor units of the puborectalis muscle can be detected with non-invasive electromyography (EMG) and to evaluate differences in electrophysiological properties of the puborectalis muscles in a small group of healthy and pathologic subjects.

METHODS

Multichannel EMG was recorded by means of a flexible probe applied on the gloved index finger and carrying an array of eight equally spaced (1.15 mm) electrodes. A multichannel EMG amplifier provided seven outputs corresponding to the pairs of adjacent electrodes. Tests were performed in three different positions (dorsal, left and right) over the puborectalis muscle on 20 subjects (nine healthy, seven constipated and four incontinent patients). Motor unit action potentials (MUAPs) generated at the innervation zone of a MU and propagating along the muscle fibers generated repetitive characteristic patterns on the seven output channels allowing identification of anatomical features of the motor units.

RESULTS

MUAPs were observed travelling in either one or both directions with the array in dorsal position, and mainly in ventral-to-dorsal direction in either lateral position. MUAP amplitude was lower in constipated and incontinent patients with respect to healthy subjects. The conduction velocity estimated on the identified MUAPs was lower for constipated patients with respect to healthy subjects suggesting different mechanical properties of the active motor units.

CONCLUSIONS

This technique allows the extraction of relevant information about the anatomical features (innervation zone position and overlapping of motor unit branches) of the puborectalis muscle and its electrophysiological properties and maybe can be applied as an novel methodology for assessing the anorectal function in patients.

Detecting the unique representation of motor-unit action potentials in the surface electromyogram

This study investigated the relative proportion of motor-unit action potentials that are uniquely represented in the simulated and experimental surface electromyogram (EMG). Two hundred motor units were simulated in a cylindrical anatomical system. Action potentials for each motor unit were generated with a model and then compared with those of other motor units. Pairs of motor units were considered indistinguishable and the motor units not uniquely represented in the surface EMG, when the difference in the mean energy for the pair of potentials was <5%. The anatomical conditions and recording configurations had a substantial influence on the percentage of motor units that could be uniquely identified in the simulated EMG. For example, a single monopolar channel could discriminate only 3.4% of motor units in the simulated population, whereas a system with 81 Laplacian channels arranged in a grid could discriminate 83.8% of the motor units under the same conditions. The simulation results were confirmed with populations of motor units recorded experimentally from the abductor digiti minimi muscle of eight healthy men. Furthermore, the relative proportion of uniquely identified motor units in the simulated signal was only moderately related to motor-unit size and distance from the electrodes. These results indicate the upper limit for detection of individual motor units from the surface EMG and show that a few channels of surface EMG recordings are not sufficient to study single motor units. The noninvasive identification of motor units from the surface EMG requires the use of multiple channels of information.

Amplitude cancellation of motor-unit action potentials in the surface electromyogram can be estimated with spike-triggered averaging

The study presents analytical, simulation, and experimental analyses of amplitude cancellation of motor-unit action potentials (APs) in the interference electromyogram (EMG) and its relation to the size of the spike-triggered average (STA) EMG. The amount of cancellation of motor-unit APs decreases monotonically as a function of the ratio between the root mean square (RMS) of the motor-unit AP and the RMS of the interference EMG signal. The theoretical derivation of this association indicates a method to measure cancellation in individual motor units by STA of the interference and squared EMGs. The theoretical relation was examined in both simulated EMG signals generated by populations of 200 motor units and experimental recordings of 492 and 184 motor-unit APs in the vastus medialis and abductor digiti minimi muscles, respectively. Although the theoretical relation predicted (R2 = 0.95; P < 0.001) the amount of cancellation in the simulated EMGs, the presence of motor-unit synchronization decreased the strength of the association for small APs. The decrease in size of the STA obtained from the squared EMG relative to that extracted from the interference EMG was predicted by the experimental measure of cancellation (R2 = 0.65; P < 0.001, for vastus medialis; R2 = 0.26; P < 0.05, for abductor digiti minimi). The results indicate that cancellation of APs in the interference EMG can be analytically predicted and experimentally measured with STA from the discharge times of the motor units into the surface EMG.

Effect of innervation-zone distribution on estimates of average muscle-fiber conduction velocity

Muscle-fiber conduction velocity (MFCV) may be a diagnostic variable, but the factors affecting its estimate from surface electromyographic (SEMG) signals are still not fully understood. We investigated the effect of innervation-zone distribution on estimates of average MFCV from simulated SEMG signals. Single-fiber action potentials were simulated using a computer model that describes an idealized cylindrical volume conductor comprised of bone, muscle, subcutaneous, and skin tissues. A model describing the characteristics and control of a motor-neuron pool was used to simulate 60 s of SEMG. Five innervation-zone distributions, inclined with respect to the fiber direction, were simulated within the muscle tissue, based on data from the literature and an experimental analysis. Two parameters were varied concurrently: (1) excitation level (5%-100%, 5% increments); and (2) subcutaneous tissue thickness (4 and 10 mm). The MFCV estimates were affected by the distribution of innervation zones with bias varying with excitation level and thickness of subcutaneous tissue. The MFCV estimates ranged from 3.95 to 11.67 m/s for single-differential and from 3.99 to 5.23 m/s for double-differential recordings when all simulated motor units were assigned the same conduction velocity of 4 m/s. Amplitude and characteristic spectral frequencies were also influenced by the distribution of innervation zones. The effect of innervation-zone distribution on MFCV estimates is thus substantial and may be a significant confounding factor in experimental and clinical studies on muscles with diffuse innervation zones.