Reliability of muscle fiber conduction velocity in the tibialis anterior

This document could not have been completed without the hard work of a number of individuals. First and foremost, my supervisor, Dr. David Gabriel deserves the utmost recognition for the immense effort and time spent guiding the production of this document through the various stages of completion. Also, aiding in the data collection, technical support, and general thought processing were Lab Technician Greig Inglis and fellow members of the Electromyographic Kinesiology Laboratory Jon Howard, Sean Lenhardt, Lara Robbins, and Corrine Davies-Schinkel. The input of Drs. Ted Clancy, Phil Sullivan and external examiner Dr. Anita Christie, all members ofthe assessment committee, was incredibly important and vital to the completion of this work. Their expertise provided a strong source of knowledge and went to ensure that this project was completed at exemplary level. There were a number of other individuals who were an immense help in getting this project off the ground and completed. The donation of their time and efforts was very generous and much needed in order to fulfill the requirements needed for completion of this study. Finally, I cannot exclude the contributions of my family throughout this project especially that of my parents whose support never wavers

Implementing physiologically-based approaches to improve Brain-Computer Interfaces usability in post-stroke motor rehabilitation

Stroke is one of the leading causes of long-term motor disability and, as such, directly impacts on daily living activities. Identifying new strategies to recover motor function is a central goal of clinical research. In the last years the approach to the post-stroke function restore has moved from the physical rehabilitation to the evidence-based neurological rehabilitation. Brain-Computer Interface (BCI) technology offers the possibility to detect, monitor and eventually modulate brain activity. The potential of guiding altered brain activity back to a physiological condition through BCI and the assumption that this recovery of brain activity leads to the restoration of behaviour is the key element for the use of BCI systems for therapeutic purposes. To bridge the gap between research-oriented methodology in BCI design and the usability of a system in the clinical realm requires efforts towards BCI signal processing procedures that would optimize the balance between system accuracy and usability. The thesis focused on this issue and aimed to propose new algorithms and signal processing procedures that, by combining physiological and engineering approaches, would provide the basis for designing more usable BCI systems to support post-stroke motor recovery. Results showed that introduce new physiologically-driven approaches to the pre-processing of BCI data, methods to support professional end-users in the BCI control parameter selection according to evidence-based rehabilitation principles and algorithms for the parameter adaptation in time make the BCI technology more affordable, more efficient, and more usable and, therefore, transferable to the clinical realm

Evaluation of Respiratory Muscle Activity by Means of Concentric Ring Electrodes

© 2021 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Surface electromyography (sEMG) can be used for the evaluation of respiratory muscle activity. Recording sEMG involves the use of surface electrodes in a bipolar configuration. However, electrocardiographic (ECG) interference and electrode orientation represent considerable drawbacks to bipolar acquisition. As an alternative, concentric ring electrodes (CREs) can be used for sEMG acquisition and offer great potential for the evaluation of respiratory muscle activity due to their enhanced spatial resolution and simple placement protocol, which does not depend on muscle fiber orientation. The aim of this work was to analyze the performance of CREs during respiratory sEMG acquisitions. Respiratory muscle sEMG was applied to the diaphragm and sternocleidomastoid muscles using a bipolar and a CRE configuration. Thirty-two subjects underwent four inspiratory load spontaneous breathing tests which was repeated after interchanging the electrode positions. We calculated parameters such as (1) spectral power and (2) median frequency during inspiration, and power ratios of inspiratory sEMG without ECG in relation to (3) basal sEMG without ECG (R-ins/noise), (4) basal sEMG with ECG (R-ins/cardio) and (5) expiratory sEMG without ECG (R-ins/exp). Spectral power, R-ins/noise and R-ins/cardio increased with the inspiratory load. Significantly higher values (p < 0.05) of R-ins/cardio and significantly higher median frequencies were obtained for CREs. R-ins/noise and R-ins/exp were higher for the bipolar configuration only in diaphragm sEMG recordings, whereas no significant differences were found in the sternocleidomastoid recordings. Our results suggest that the evaluation of respiratory muscle activity by means of sEMG can benefit from the remarkably reduced influence of cardiac activity, the enhanced detection of the shift in frequency content and the axial isotropy of CREs which facilitates its placement.This work was supported in part by the CERCA Program/Generalitat de Catalunya, in part by the Secretaria d'Universitats i Recerca de la Generalitat de Catalunya under Grant GRC 2017 SGR 01770, in part by the Spanish Grants RTI2018-098472-B-I00, RTI2018-094449-A-I00-AR (MCIU/AEI/FEDER, UE) and DPI2015-68397-R (MINECO/FEDER), and in part by the Biomedical Research Networking Centre in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN, Instituto de Salud Carlos III/FEDER). The first author was supported by the IFARHU-SENACYT Scholarship Program from the Panama Government under Grant 270-2012-273.Estrada-Petrocelli, L.; Torres, A.; Sarlabous, L.; Ràfols-De-Urquía, M.; Ye Lin, Y.; Prats-Boluda, G.; Jané, R.... (2021). Evaluation of Respiratory Muscle Activity by Means of Concentric Ring Electrodes. IEEE Transactions on Biomedical Engineering. 68(3):1005-1014. https://doi.org/10.1109/TBME.2020.3012385S1005101468

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

Evaluation of Swallowing Related Muscle Activity by Means of Concentric Ring Electrodes

[EN] Surface electromyography (sEMG) can be helpful for evaluating swallowing related muscle activity. Conventional recordings with disc electrodes suffer from significant crosstalk from adjacent muscles and electrode-to-muscle fiber orientation problems, while concentric ring electrodes (CREs) offer enhanced spatial selectivity and axial isotropy. The aim of this work was to evaluate CRE performance in sEMG recordings of the swallowing muscles. Bipolar recordings were taken from 21 healthy young volunteers when swallowing saliva, water and yogurt, first with a conventional disc and then with a CRE. The signals were characterized by the root-mean-square amplitude, signal-to-noise ratio, myopulse, zero-crossings, median frequency, bandwidth and bilateral muscle cross-correlations. The results showed that CREs have advantages in the sEMG analysis of swallowing muscles, including enhanced spatial selectivity and the associated reduction in crosstalk, the ability to pick up a wider range of EMG frequency components and easier electrode placement thanks to its radial symmetry. However, technical changes are recommended in the future to ensure that the lower CRE signal amplitude does not significantly affect its quality. CREs show great potential for improving the clinical monitoring and evaluation of swallowing muscle activity. Future work on pathological subjects will assess the possible advantages of CREs in dysphagia monitoring and diagnosis.This work was supported by the Spanish Ministry of the Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR).Garcia-Casado, J.; Prats-Boluda, G.; Ye Lin, Y.; Restrepo-Agudelo, S.; Perez-Giraldo, E.; Orozco-Duque, A. (2020). 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Biological Cybernetics, 86(6), 445-456. doi:10.1007/s00422-002-0309-2Garcia-Casado, J., Ye-Lin, Y., Prats-Boluda, G., & Makeyev, O. (2019). Evaluation of Bipolar, Tripolar, and Quadripolar Laplacian Estimates of Electrocardiogram via Concentric Ring Electrodes. Sensors, 19(17), 3780. doi:10.3390/s19173780Toole, C., Martinez-Juárez, I. E., Gaitanis, J. N., Blum, A., Sunderam, S., Ding, L., … Besio, W. G. (2019). Source localization of high-frequency activity in tripolar electroencephalography of patients with epilepsy. Epilepsy & Behavior, 101, 106519. doi:10.1016/j.yebeh.2019.106519Lidón-Roger, J., Prats-Boluda, G., Ye-Lin, Y., Garcia-Casado, J., & Garcia-Breijo, E. (2018). Textile Concentric Ring Electrodes for ECG Recording Based on Screen-Printing Technology. Sensors, 18(1), 300. doi:10.3390/s18010300Aghaei-Lasboo, A., Inoyama, K., Fogarty, A. S., Kuo, J., Meador, K. J., Walter, J. J., … Fisher, R. S. (2020). Tripolar concentric EEG electrodes reduce noise. Clinical Neurophysiology, 131(1), 193-198. doi:10.1016/j.clinph.2019.10.022Li, G., Wang, Y., Lin, L., Jiang, W., Wang, L. L., Lu, S. C.-Y., & Besio, W. G. (2005). Active Laplacian electrode for the data-acquisition system of EHG. Journal of Physics: Conference Series, 13, 330-335. doi:10.1088/1742-6596/13/1/077Ye-Lin, Y., Alberola-Rubio, J., Prats-boluda, G., Perales, A., Desantes, D., & Garcia-Casado, J. (2014). Feasibility and Analysis of Bipolar Concentric Recording of Electrohysterogram with Flexible Active Electrode. Annals of Biomedical Engineering, 43(4), 968-976. doi:10.1007/s10439-014-1130-5Zena-Giménez, V., Garcia-Casado, J., Ye-Lin, Y., Garcia-Breijo, E., & Prats-Boluda, G. (2018). A Flexible Multiring Concentric Electrode for Non-Invasive Identification of Intestinal Slow Waves. Sensors, 18(2), 396. doi:10.3390/s18020396Garcia-Casado, J., Zena-Gimenez, V., Prats-Boluda, G., & Ye-Lin, Y. (2013). Enhancement of Non-Invasive Recording of Electroenterogram by Means of a Flexible Array of Concentric Ring Electrodes. Annals of Biomedical Engineering, 42(3), 651-660. doi:10.1007/s10439-013-0935-yCastroflorio, T., Deregibus, A., Bargellini, A., Debernardi, C., & Manfredini, D. (2014). Detection of sleep bruxism: comparison between an electromyographic and electrocardiographic portable holter and polysomnography. Journal of Oral Rehabilitation, 41(3), 163-169. doi:10.1111/joor.12131Lee, Y., Nicholls, B., Sup Lee, D., Chen, Y., Chun, Y., Siang Ang, C., & Yeo, W.-H. (2017). Soft Electronics Enabled Ergonomic Human-Computer Interaction for Swallowing Training. Scientific Reports, 7(1). doi:10.1038/srep46697Kim, M. K., Kantarcigil, C., Kim, B., Baruah, R. K., Maity, S., Park, Y., … Lee, C. H. (2019). Flexible submental sensor patch with remote monitoring controls for management of oropharyngeal swallowing disorders. 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Development of sEMG sensors and algorithms for silent speech recognition. Journal of Neural Engineering, 15(4), 046031. doi:10.1088/1741-2552/aac965Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012). Feature reduction and selection for EMG signal classification. Expert Systems with Applications, 39(8), 7420-7431. doi:10.1016/j.eswa.2012.01.102Liu, X., Makeyev, O., & Besio, W. (2020). Improved Spatial Resolution of Electroencephalogram Using Tripolar Concentric Ring Electrode Sensors. Journal of Sensors, 2020, 1-9. doi:10.1155/2020/6269394Farina, D., Arendt-Nielsen, L., Merletti, R., Indino, B., & Graven-Nielsen, T. (2003). Selectivity of spatial filters for surface EMG detection from the tibialis anterior muscle. IEEE Transactions on Biomedical Engineering, 50(3), 354-364. doi:10.1109/tbme.2003.808830Wang, K., Parekh, U., Pailla, T., Garudadri, H., Gilja, V., & Ng, T. N. (2017). 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A finite element model for the investigation of surface EMG signals during dynamic contraction

A finite element (FE) model for the generation of single fiber action potentials (SFAPs) in a muscle undergoing various degrees of fiber shortening has been developed. The muscle is assumed to be fusiform with muscle fibers following a curvilinear path described by a Gaussian function. Different degrees of fiber shortening are simulated by changing the parameters of the fiber path and maintaining the volume of the muscle constant. The conductivity tensor is adapted to the muscle fiber orientation. At each point of the volume conductor, the conductivity of the muscle tissue in the direction of the fiber is larger than that in the transversal direction. Thus, the conductivity tensor changes point-by-point with fiber shortening, adapting to the fiber paths. An analytical derivation of the conductivity tensor is provided. The volume conductor is then studied with an FE approach using the analytically derived conductivity tensor (Mesin, Joubert, Hanekom, Merletti&Farina 2006). Representative simulations of SFAPs with the muscle at different degrees of shortening are presented. It is shown that the geometrical changes in the muscle, which imply changes in the conductivity tensor, determine important variations in action potential shape, thus affecting its amplitude and frequency content. The model is expanded to include the simulation of motor unit action potentials (MUAPs). Expanding the model was done by assigning each single fiber (SF) in the motor unit (MU) a random starting position chosen from a normal distribution. For the model 300 SFs are included in an MU, with an innervation zone spread of 12 mm. Only spatial distribution was implemented. Conduction velocity (CV) was the same for all fibers of the MU. Representative simulations for the MUAPs with the muscle at different degrees of shortening are presented. The influence of interelectrode distance and angular displacement are also investigated as well as the influence of the inclusion of the conductivity tensor. It has been found that the interpretation of surface electromyography during movement or joint angle change is complicated owing to geometrical artefacts i.e. the shift of the electrodes relative to the muscle fibers and also because of the changes in the conductive properties of the tissue separating the electrode from the muscle fibers. Detection systems and electrode placement should be chosen with care. The model provides a new tool for interpreting surface electromyography (sEMG) signal features with changes in muscle geometry, as happens during dynamic contractions.Dissertation (MEng (Bio-Engineering))--University of Pretoria, 2008.Electrical, Electronic and Computer EngineeringMEng (Bio-Engineering)unrestricte

Perturbation Based Decomposition of sEMG Signals

Surface electromyography records the motor unit action potential signals in the vicinity of the electrode to reveal information on muscle activation. Decomposition of sEMG signals for characterization of constituent motor unit action potentials in terms of amplitude and firing times is useful for clinical research as well as diagnosis of neurological disorders. Successful decomposition of sEMG signals would allow for pertinent motor unit action potential information to be acquired without discomfort to the subject or the need for a well-trained operator (compared with intramuscular EMG). To determine amplitudes and firing times for motor unit action potentials in an sEMG recording, Szlavik\u27s perturbation based decomposition may be applied. The decomposition was initially applied to synthetic sEMG signals and then to experimental data collected from the biceps brachii. Szlavik\u27s decomposition estimator yields satisfactory results for synthetic and experimental sEMG signals with reasonable complexity

A Multiple Instance Learning Approach to Electrophysiological Muscle Classification for Diagnosing Neuromuscular Disorders Using Quantitative EMG

Neuromuscular disorder is a broad term that refers to diseases that impair muscle functionality either by affecting any part of the nerve or muscle. Electrodiagnosis of most neuromuscular disorders is based on the electrophysiological classification of involved muscles which in turn, is performed by inferring the structure and function of the muscles by analyzing electromyographic (EMG) signals recorded during low to moderate levels of contraction. The functional unit of muscle contraction is called a motor unit (MU). The morphology and physiology of the MUs of an examined muscle are inferred by extracting motor unit potentials (MUPs) from the EMG signals detected from the muscle. As such, electrophysiological muscle classification is performed by first characterizing extracted MUPs and then aggregating these characterizations. The task of classifying muscles can be represented as an instance of a multiple instance learning (MIL) problem. In the MIL paradigm, a bag of instances shares a label and the instance labels are hidden, contrary to standard supervised learning, where each training instance is labeled. In MIL-based muscle classification, the instances are the MUPs extracted from the EMG signals of the analyzed muscle and the bag is the muscle. Detecting and counting the MUPs indicating a specific category of a neuromuscular disorder can result in accurately classifying the examined muscle. As such, three major issues usually arise: how to infer MUP labels without full supervision; how the cardinality relationships between MUP labels contribute to predict the muscle label; and how the muscle as a whole entity is classified. In this thesis, these three challenges are addressed. To this end, an MIL-based muscle classification system is proposed that has five major steps: 1) MUPs are represented using morphological, stability, and novel near fiber parameters as well as spectral features extracted from wavelet coefficients. This representation helps to analyze MUPs from a variety of aspects. 2) MUP feature selection using unsupervised similarity preserving Laplacian score which is independent of any learning algorithm. Hence, the features selected in this work can be used in other electrophysiological muscle classification systems. 3) MUP clustering using a novel clustering algorithm called Neighbourhood Distance Entropy Consistency (NDEC) which contributes to solve the traditional problem of finding representations of MUP normality and abnormality and provides a dynamic number of MUP characterization classes which will be used instead of the conventional three classes (i.e. normal, myopathic, and neurogenic). This clustering was performed to highlight the effects of disease on both fiber spatial distributions and fiber diameter distributions, which lead to a continuity of MUP characteristics. These clusters can potentially represent several concepts of MUP normality and abnormality. 4) Muscle representation by embedding its MUP cluster associations in a feature vector, and 5) Muscle classification using support vector machines or random forests. Quantitative results obtained by applying the proposed method to four electrophysiologically different groups of muscles including proximal arm, proximal leg, distal arm, and distal leg show the superior and stable performance of the proposed muscle classification system compared to previous works. Additionally, modelling electrophysiological muscle classification as an instance of the MIL can solve the traditional problem of characterizing MUPs without full supervision. The proposed clustering algorithm in this work, can be used as an effective technique in other pattern recognition and medical diagnostic systems in which discovering natural clusters within data is a necessity

Contribution to the Study of the Direct and Inverse Problem in Electromyography (EMG)

RÉSUMÉ On dispose maintenant de prothèses myoélectriques du membre supérieur pouvant produire plusieurs mouvements utilisés dans les activités de la vie quotidienne. Pour les activer, la présence de 6 compartiments anatomiques dans le biceps brachial pourrait être exploitée. Pour aider à vérifier si ces compartiments ont pu être activés lors de contractions accomplies par des sujets normaux, l'utilisation d'un modèle direct et inverse pourrait être très utile. Pour initier le développement de ces modèles, des données provenant d'expériences où, un contenant cylindrique, encerclé de 16 électrodes et rempli d'une solution saline a été utilisé. Dsns ce bassin, jusqu'à 3 dipôles avaient été introduits à des positions connues. Pour poursuivre la validation du modèle inverse, on a simulé des signaux associés à 3 regroupements de 5000 fibres musculaires placées à des positions connues à l'intérieur d'un bassin cylindrique virtuel. Finalement, les signaux EMG recueillis au-dessus du biceps de sujets normaux lors d'expériences visant à activer individuellement ou en groupe les 6 compartiments du biceps ont été analysés. On a identifié 3 zones d'activité: une dans le chef court et 2 dans le chef long du muscle. Dans le chef court, l'intensité du dipôle a été similaire dans les 6 conditions testées tandis que sa position était variable. Dans le chef long, la position des deux zones actives est moins variable mais leur intensité très variable. Alors que les 3 zones d'activité peuvent être considérées comme étant situées dans divers compartiments du biceps, on a trouvé que leur position lorsque reportée sur une illustration générique d'une coupe transversale de l'avant-bras, l'une des zones était localisée au niveau de la couche de graisse et celle de la peau. Il semble donc possible d'identifier dans le biceps des zones d'activité associables à ses compartiments. Toutefois étant donné que certaines de ces zones pourraient se situer en dehors du muscle, la présence des couches de gras et de la peau doivent être introduites pour un modèle plus réaliste du bras lors d’études portant sur le problème direct et inverse en EMG.----------ABSTRACT In the context of improving the control of upper arm myoelectric prostheses capable of producing various useful movements for daily life activities, the presence of up to 6 anatomical compartments within the biceps brachii can be exploited to increase the number of potential control sites. To help identify where activity could occurs within the biceps during different contractions accomplished by normal subjects, a direct and an inverse model could be very useful. To develop such models, we started with the reproduction of previously collected data obtained with 16 equally spaced electrodes circling a tank filled with a saline solution. Up to 3 dipoles were placed at known positions within the tank. To further test the inverse model, simulated data obtained from the activity of 3 groups of 5000 closely packed single fibers placed at known positions within a virtual tank similar to the real one were analyzed. Finally, EMG signals collected over the biceps brachii during experiments aimed at activating individually or in groups the 6 compartments of the biceps where analyzed in 6 conditions. Three zones of activity were found: one in the short head of the muscle and 2 in its long head. In the short head, the dipole intensity was similar in the 6 conditions tested while its position was variable. In the long head, the position of the two active zones was less variable but their intensity was quite variable. While those 3 zones of activity could be considered to be located in 3 of the 6 muscle’s compartments, when their position was overlaid on a generic cross-section image of the upper arm, one of the zones was outside the muscle tissue. It thus appears possible within the biceps, to identify active zones associable to the muscle’s compartments. However, considering that some of the detected positions could be over the fat and skin layers, those layers should be introduced for a more realistic upper arm model when studying the EMG direct and inverse problem