Detection of multiple innervation zones from multi-channel surface EMG recordings with low signal-to-noise ratio using graph-cut segmentation

Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies.The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the Seventh Framework Programme of the European Union (FP7/2007-2013) under REA grant agreement no. 600388 (TECNIOspring programme), from the Agency for Business Competitiveness of the Government of Catalonia, ACCIÓ, and from Spanish Ministry of Economy and Competitiveness- Spain (project DPI2014-59049-R).Peer ReviewedPostprint (published version

Analysis of forearm muscles activity by means of new protocols of multichannel EMG signal recording and processing

Los movimientos voluntarios del cuerpo son controlados por el sistema nervioso central y periférico a través de la contracción de los músculos esqueléticos. La contracción se inicia al liberarse un neurotransmisor sobre la unión neuromuscular, iniciando la propagación de un biopotencial sobre la membrana de las fibras musculares que se desplaza hacia los tendones: el Potencial de Acción de la Unidad Motora (MUAP). La señal electromiográfica de superficie registra la activación continua de dichos potenciales sobre la superficie de la piel y constituye una valiosa herramienta para la investigación, diagnóstico y seguimiento clínico de trastornos musculares, así como para la identificación de la intención movimiento tanto en términos de dirección como de potencia. En el estudio de las enfermedades del sistema neuromuscular es necesario analizar el nivel de actividad, la capacidad de producción de fuerza, la activación muscular conjunta y la predisposición a la fatiga muscular, todos ellos asociados con factores fisiológicos que determinan la resultante contracción mioeléctrica. Además, el uso de matrices de electrodos facilita la investigación de las propiedades periféricas de las unidades motoras activas, las características anatómicas del músculo y los cambios espaciales en su activación, ocasionados por el tipo de tarea motora o la potencia de la misma. El objetivo principal de esta tesis es el diseño e implementación de protocolos experimentales y algoritmos de procesado para extraer información fiable de señales sEMG multicanal en 1 y 2 dimensiones del espacio. Dicha información ha sido interpretada y relacionada con dos patologías específicas de la extremidad superior: Epicondilitis Lateral y Lesión de Esfuerzo Repetitivo. También fue utilizada para identificar la dirección de movimiento y la fuerza asociada a la contracción muscular, cuyos patrones podrían ser de utilidad en aplicaciones donde la señal electromiográfica se utilice para controlar interfaces hombre-máquina como es el caso de terapia física basada en robots, entornos virtuales de rehabilitación o realimentación de la actividad muscular. En resumen, las aportaciones más relevantes de esta tesis son: * La definición de protocolos experimentales orientados al registro de señales sEMG en una región óptima del músculo. * Definición de índices asociados a la co-activación de diferentes músculos * Identificación de señales artefactuadas en registros multicanal * Selección de los canales mas relevantes para el análisis Extracción de un conjunto de características que permita una alta exactitud en la identificación de tareas motoras Los protocolos experimentales y los índices propuestos permitieron establecer que diversos desequilibrios entre músculos extrínsecos del antebrazo podrían desempeñar un papel clave en la fisiopatología de la epicondilitis lateral. Los resultados fueron consistentes en diferentes ejercicios y pueden definir un marco de evaluación para el seguimiento y evaluación de pacientes en programas de rehabilitación motora. Por otra parte, se encontró que las características asociadas con la distribución espacial de los MUAPs mejoran la exactitud en la identificación de la intención de movimiento. Lo que es más, las características extraídas de registros sEMG de alta densidad son más robustas que las extraídas de señales bipolares simples, no sólo por la redundancia de contacto implicada en HD-EMG, sino también porque permite monitorizar las regiones del músculo donde la amplitud de la señal es máxima y que varían con el tipo de ejercicio, permitiendo así una mejor estimación de la activación muscular mediante el análisis de los canales mas relevantes.Voluntary movements are achieved by the contraction of skeletal muscles controlled by the Central and Peripheral Nervous system. The contraction is initiated by the release of a neurotransmitter that promotes a reaction in the walls of the muscular fiber, producing a biopotential known as Motor Unit Action Potential (MUAP) that travels from the neuromuscular junction to the tendons. The surface electromyographic signal records the continuous activation of such potentials over the surface of the skin and constitutes a valuable tool for the diagnosis, monitoring and clinical research of muscular disorders as well as to infer motion intention not only regarding the direction of the movement but also its power. In the study of diseases of the neuromuscular system it is necessary to analyze the level of activity, the capacity of production of strength, the load-sharing between muscles and the probably predisposition to muscular fatigue, all of them associated with physiological factors determining the resultant muscular contraction. Moreover, the use of electrode arrays facilitate the investigation of the peripheral properties of the active Motor Units, the anatomical characteristics of the muscle and the spatial changes induced in their activation of as product of type of movement or power of the contraction.The main objective of this thesis was the design and implementation of experimental protocols, and algorithms to extract information from multichannel sEMG signals in 1 and 2 dimensions of the space. Such information was interpreted and related to pathological events associated to two upper-limb conditions: Lateral Epicondylitis and Repetitive Strain Injury. It was also used to identify the direction of movement and contraction strength which could be useful in applications concerning the use of biofeedback from EMG like in robotic- aided therapies and computer-based rehabilitation training.In summary, the most relevant contributions are:§The definition of experimental protocols intended to find optimal regions for the recording of sEMG signals. §The definition of indices associated to the co- activation of different muscles. §The detection of low-quality signals in multichannel sEMG recordings.§ The selection of the most relevant EMG channels for the analysis§The extraction of a set of features that led to high classification accuracy in the identification of tasks.The experimental protocols and the proposed indices allowed establishing that imbalances between extrinsic muscles of the forearm could play a key role in the pathophysiology of lateral epicondylalgia. Results were consistent in different types of motor task and may define an assessment framework for the monitoring and evaluation of patients during rehabilitation programs.On the other hand, it was found that features associated with the spatial distribution of the MUAPs improve the accuracy of the identification of motion intention. What is more, features extracted from high density EMG recordings are more robust not only because it implies contact redundancy but also because it allows the tracking of (task changing) skin surface areas where EMG amplitude is maximal and a better estimation of muscle activity by the proper selection of the most significant channels

Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors

Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG

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

A denoising algorithm for surface EMG decomposition

The goal of the present thesis was to investigate a novel motor unit potential train (MUPT) editing routine, based on decreasing the variability in shape (variance ratio, VR) of the MUP ensemble. Decomposed sEMG data from 20 participants at 60% MVC of wrist flexion was used. There were two levels of denoising (relaxed and strict) criteria for removing discharge times associated with waveforms that did not decrease the VR and increase its signal-to-noise ratio (SNR) of the MUP ensemble. The peak-to-peak amplitude and the duration between the positive and negative peaks for the MUP template were dependent on the level of denoising (p’s 0.05). The same was true between denoising criteria (p>0.05). Editing the MUPT based on MUP shape resulted in significant differences in measures extracted from the MUP template, with trivial difference between the standard error of estimate for mean IDIs between the complete and denoised MUPTs

Regional vastus medialis and vastus lateralis activation in females with patellofemoral pain

Introduction This study aimed to investigate whether regional activation patterns in the vasti muscles differ between females with and without patellofemoral pain (PFP), and whether muscle activation patterns correlate with knee extension strength. Methods Thirty-six females with PFP and 20 pain-free controls performed a standardized knee flexion-extension task. The activation of vastus medialis (VM) and vastus lateralis (VL) was collected using high-density surface EMG and analyzed using principal component (PC) analysis. Spatial locations and temporal coefficients of the PC, and the percent variance they explain, were compared between groups and between the concentric and the eccentric phases of the movement. Correlations were assessed between PC features and knee extension strength. Results The spatial weights of PC1 (general vasti activation) and PC2 (reflecting vastus-specific activation) were similar between groups (R > 0.95). Activation patterns in PFP were less complex than controls. Fewer PC features were necessary to reconstruct 90% of the signal for PFP participants in the concentric phase (P < 0.05), and the difference in bias of activation to VM (concentric phase) or VL (eccentric phase) was less between phases for PFP participants (P < 0.05). Smaller difference in vastus-specific activation in concentric and eccentric phases (less task specificity of VM/VL coordination) was related to greater maximal knee extension strength (P < 0.05,

Alignment of magnetic sensing and clinical magnetomyography

Neuromuscular diseases are a prevalent cause of prolonged and severe suffering for patients, and with the global population aging, it is increasingly becoming a pressing concern. To assess muscle activity in NMDs, clinicians and researchers typically use electromyography (EMG), which can be either non-invasive using surface EMG, or invasive through needle EMG. Surface EMG signals have a low spatial resolution, and while the needle EMG provides a higher resolution, it can be painful for the patients, with an additional risk of infection. The pain associated with the needle EMG can pose a risk for certain patient groups, such as children. For example, children with spinal muscular atrophy (type of NMD) require regular monitoring of treatment efficacy through needle EMG; however, due to the pain caused by the procedure, clinicians often rely on a clinical assessment rather than needle EMG. Magnetomyography (MMG), the magnetic counterpart of the EMG, measures muscle activity non-invasively using magnetic signals. With super-resolution capabilities, MMG has the potential to improve spatial resolution and, in the meantime, address the limitations of EMG. This article discusses the challenges in developing magnetic sensors for MMG, including sensor design and technology advancements that allow for more specific recordings, targeting of individual motor units, and reduction of magnetic noise. In addition, we cover the motor unit behavior and activation pattern, an overview of magnetic sensing technologies, and evaluations of wearable, non-invasive magnetic sensors for MMG

Kinematics of individual muscle units in natural contractions measured in vivo using ultrafast ultrasound

Objective. The study of human neuromechanical control at the motor unit (MU) level has predominantly focussed on electrical activity and force generation, whilst the link between these, i.e. the muscle deformation, has not been widely studied. To address this gap, we analysed the kinematics of muscle units in natural contractions. Approach. We combined high-density surface electromyography (HDsEMG) and ultrafast ultrasound (US) recordings, at 1000 frames per second, from the tibialis anterior muscle to measure the motion of the muscular tissue caused by individual MU contractions. The MU discharge times were identified online by decomposition of the HDsEMG and provided as biofeedback to 12 subjects who were instructed to keep the MU active at the minimum discharge rate (9.8 ± 4.7 pulses per second; force less than 10% of the maximum). The series of discharge times were used to identify the velocity maps associated with 51 single muscle unit movements with high spatio-temporal precision, by a novel processing method on the concurrently recorded US images. From the individual MU velocity maps, we estimated the region of movement, the duration of the motion, the contraction time, and the excitation–contraction (E–C) coupling delay. Main results. Individual muscle unit motions could be reliably identified from the velocity maps in 10 out of 12 subjects. The duration of the motion, total contraction time, and E–C coupling were 17.9 $\pm$ 5.3 ms, 56.6 $\pm$ 8.4 ms, and 3.8 $\pm$ 3.0 ms (n = 390 across ten participants). The experimental measures also provided the first evidence of muscle unit twisting during voluntary contractions and MU territories with distinct split regions. Significance. The proposed method allows for the study of kinematics of individual MU twitches during natural contractions. The described measurements and characterisations open new avenues for the study of neuromechanics in healthy and pathological conditions