Surface EMG and muscle fatigue: multi-channel approaches to the study of myoelectric manifestations of muscle fatigue

In a broad view, fatigue is used to indicate a degree of weariness. On a muscular level, fatigue posits the reduced capacity of muscle fibres to produce force, even in the presence of motor neuron excitation via either spinal mechanisms or electric pulses applied externally. Prior to decreased force, when sustaining physically demanding tasks, alterations in the muscle electrical properties take place. These alterations, termed myoelectric manifestation of fatigue, can be assessed non-invasively with a pair of surface electrodes positioned appropriately on the target muscle; traditional approach. A relatively more recent approach consists of the use of multiple electrodes. This multi-channel approach provides access to a set of physiologically relevant variables on the global muscle level or on the level of single motor units, opening new fronts for the study of muscle fatigue; it allows for: (i) a more precise quantification of the propagation velocity, a physiological variable of marked interest to the study of fatigue; (ii) the assessment of regional, myoelectric manifestations of fatigue; (iii) the analysis of single motor units, with the possibility to obtain information about motor unit control and fibre membrane changes. This review provides a methodological account on the multi-channel approach for the study of myoelectric manifestation of fatigue and on the experimental conditions to which it applies, as well as examples of their current applications

Real time estimation of generation, extinction and flow of muscle fibre action potentials in high density surface EMG

Selezionato dalla rivista COMPUTERS IN BIOLOGY AND MEDICINE come Meritorious paper per l'anno 201

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

Applications of normalised mutual information in high density surface electromyography

Normalised mutual information (NMI) is a measure derived from Shannon's entropy that has been used in a variety of fields to measure the similarity or dependence between random variables. In terms of biomedical signal processing, NMI has been used in electroencephalography to identify the functional connectivity between different regions of the brain by calculating the NMI between electrodes. Researchers have adopted this method for surface electromyography (sEMG) and have used NMI to find the functional connectivity between pairs of muscles. While these studies have been able to demonstrate that NMI can be used to measure the functional connectivity between muscles, there is little to no literature exploring other forms of sEMG signal analysis using NMI. Therefore, this research focussed on investigating alternative applications for the NMI of sEMG, the results and observations of which are discussed in this thesis.    During this research four applications for NMI were identified and investigated using high density sEMG (HD-sEMG). These applications were monitoring the progression of muscle fatigue, estimating the border between superficial muscles, locating the innervation zones (IZ) of a muscle, and identifying noisy electrodes. Initially, a method was developed to analyse HD-sEMG data using NMI, this method created NMI distributions which describe the similarity of each electrode with every other electrode. In order to summarise the NMI distributions, two additional methods were developed. The first method produced interaction maps which illustrate the number of electrodes that are similar to each electrode. The second method produced total NMI magnitude maps which show the similarity of each electrode with all the other electrodes through a sum of the NMI distributions. These methods were used to observe how muscle fatigue, IZs, noisy electrodes, and multiple muscle masses affected the NMI between electrodes. For each of the applications these observations were then used to determine whether the NMI was an appropriate measure. In each case changes in the NMI between electrodes were observed. For muscle fatigue the NMI was shown to significantly increase as the muscles fatigued, while the effect of contraction strength did not have a significant effect. This significant increase was observed in row wise electrode pairs, column wise electrode pairs, interaction maps, and total NMI magnitude maps. When an electrode array was positioned over an IZ changes in the NMI distribution shape were observed around the estimated location of the IZ. Similarly, when noise was introduced to the HD-sEMG recordings the NMI distributions of the noisy electrodes were significantly affected. And, placing the electrode array across two muscles showed that the NMI distributions of the electrodes from each muscle were distinctly dissimilar.   Based on these observations a method for identifying noisy electrodes was developed and tested using artificial data. This method was able to achieve an average accuracy above 90% for most scenarios. Another method was then developed that used the intersections between all NMI distributions to estimate the location of the border between the two muscles. It was able to achieve an average accuracy above 80% during strong contractions with 50% of the false positives being within 10mm of the target. All these results demonstrate that NMI has the potential to be used in a variety of applications outside of the functional connectivity when analysing sEMG signals. Additionally, these observations demonstrate how the NMI can change spatially over a muscle and that the value can change drastically depending on the inter-electrode distance and the electrode's position relative to the muscle

Investigation into the control of an upper-limb myoelectric prosthesis

SIGLEAvailable from British Library Document Supply Centre- DSC:DXN053608 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

A finite element approach to study skeletal muscle tissue

This dissertation investigates force generation in muscle using a finite element (FE) approach to model electrical activity and mechanical force production within skeletal muscle. The work proposes new FE models design/formulations to answer specific research questions related to skeletal muscle properties. The focus is on two specific determinants of skeletal muscle force: the activation and the connective tissue. A FE model was created and designed to study the impact of the dielectric and geometric (pennation) properties of the muscle tissues on the electric activation signal detected on the skin surface by bipolar electrodes (surface electromyography, sEMG). The model shows that when considering parallel muscle fibres the tissue, attenuated mainly frequencies in the physiological range (92-542 Hz). This study revealed a strong impact of the muscle fibres pennation angle, on the detected signal (low pass filtering effect); suggesting that the low pass filtering behaviour observed in experimental data is due to the geometry (curvature or pennation) rather than the dielectric properties. The model informed recommendations for sEMG experimental protocol to increase the inter-electrodes distance when measuring sEMG of pennated muscles. A micromechanical model of the muscle tissue was created to explore the influence of the connective tissue properties (endomysium) on the total muscle force production. The constitutive model was used to study the mechanical consequence of clustering of fibres due to the remodelling of the motor units, which occurs with ageing. An FE model with a bundle of 19 fibres was designed and simulated activating 21% and 37% of the fibres in a distributed and clustered pattern. Results showed for both activation levels that the pattern of the strain distribution changed with an increased deformation toward the centre of the bundle. This could lead to excessive unbalanced stresses if higher deformations are involved. The micromechanical model can be used to study muscle force determinants at a fascicle level. It showed the importance of the fibre distribution during the muscle activation and the consequences of age related alterations on force production

Statistical and Graph-Based Signal Processing: Fundamental Results and Application to Cardiac Electrophysiology

The goal of cardiac electrophysiology is to obtain information about the mechanism, function, and performance of the electrical activities of the heart, the identification of deviation from normal pattern and the design of treatments. Offering a better insight into cardiac arrhythmias comprehension and management, signal processing can help the physician to enhance the treatment strategies, in particular in case of atrial fibrillation (AF), a very common atrial arrhythmia which is associated to significant morbidities, such as increased risk of mortality, heart failure, and thromboembolic events. Catheter ablation of AF is a therapeutic technique which uses radiofrequency energy to destroy atrial tissue involved in the arrhythmia sustenance, typically aiming at the electrical disconnection of the of the pulmonary veins triggers. However, recurrence rate is still very high, showing that the very complex and heterogeneous nature of AF still represents a challenging problem. Leveraging the tools of non-stationary and statistical signal processing, the first part of our work has a twofold focus: firstly, we compare the performance of two different ablation technologies, based on contact force sensing or remote magnetic controlled, using signal-based criteria as surrogates for lesion assessment. Furthermore, we investigate the role of ablation parameters in lesion formation using the late-gadolinium enhanced magnetic resonance imaging. Secondly, we hypothesized that in human atria the frequency content of the bipolar signal is directly related to the local conduction velocity (CV), a key parameter characterizing the substrate abnormality and influencing atrial arrhythmias. Comparing the degree of spectral compression among signals recorded at different points of the endocardial surface in response to decreasing pacing rate, our experimental data demonstrate a significant correlation between CV and the corresponding spectral centroids. However, complex spatio-temporal propagation pattern characterizing AF spurred the need for new signals acquisition and processing methods. Multi-electrode catheters allow whole-chamber panoramic mapping of electrical activity but produce an amount of data which need to be preprocessed and analyzed to provide clinically relevant support to the physician. Graph signal processing has shown its potential on a variety of applications involving high-dimensional data on irregular domains and complex network. Nevertheless, though state-of-the-art graph-based methods have been successful for many tasks, so far they predominantly ignore the time-dimension of data. To address this shortcoming, in the second part of this dissertation, we put forth a Time-Vertex Signal Processing Framework, as a particular case of the multi-dimensional graph signal processing. Linking together the time-domain signal processing techniques with the tools of GSP, the Time-Vertex Signal Processing facilitates the analysis of graph structured data which also evolve in time. We motivate our framework leveraging the notion of partial differential equations on graphs. We introduce joint operators, such as time-vertex localization and we present a novel approach to significantly improve the accuracy of fast joint filtering. We also illustrate how to build time-vertex dictionaries, providing conditions for efficient invertibility and examples of constructions. The experimental results on a variety of datasets suggest that the proposed tools can bring significant benefits in various signal processing and learning tasks involving time-series on graphs. We close the gap between the two parts illustrating the application of graph and time-vertex signal processing to the challenging case of multi-channels intracardiac signals

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden