Open Access
Understanding EMG PDF changes with motor unit potential amplitudes, firing rates, and noise level through EMG filling curve analysis
(IEEE, 2024-08-30) Navallas Irujo, Javier; Mariscal Aguilar, Cristina; Malanda Trigueros, Armando; Rodríguez Falces, Javier; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza
EMG filling curve characterizes the EMG filling process and EMG probability density function (PDF) shape change for the entire force range of a muscle.We aim to understand the relation between the physiological and recording variables, and the resulting EMG filling curves. We thereby present an analytical and simulation study to explain how the filling curve patterns relate to specific changes in the motor unit potential (MUP) waveforms and motor unit (MU) firing rates, the two main factors affecting the EMG PDF, but also to recording conditions in terms of noise level. We compare the analytical results with simulated cases verifying a perfect agreement with the analytical model. Finally, we present a set of real EMG filling curves with distinct patterns to explain the information about MUP amplitudes, MU firing rates, and noise level that these patterns provide in the light of the analytical study. Our findings reflect that the filling factor increases when firing rate increases or when newly recruited motor unit have potentials of smaller or equal amplitude than the former ones. On the other hand, the filling factor decreases when newly recruited potentials are larger in amplitude than the previous potentials. Filling curves are shown to be consistent under changes of the MUP waveform, and stretched under MUP amplitude scaling. Our findings also show how additive noise affects the filling curve and can even impede to obtain reliable information from the EMG PDF statistics.
Open Access
The probability density function of the surface electromyogram and its dependence on contraction force in the vastus lateralis
(BMC, 2024-10-26) Rodríguez Falces, Javier; Malanda Trigueros, Armando; Mariscal Aguilar, Cristina; Recalde Villamayor, Silvia; Navallas Irujo, Javier; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Introduction: the probability density function (PDF) of the surface electromyogram (sEMG) depends on contraction force. This dependence, however, has so far been investigated by having the subject generate force at a few fixed percentages of MVC. Here, we examined how the shape of the sEMG PDF changes with contraction force when this force was gradually increased from zero. Methods: voluntary surface EMG signals were recorded from the vastus lateralis of healthy subjects as force was increased in a continuous manner vs. in a step-wise fashion. The sEMG filling process was examined by measuring the EMG filling factor, computed from the non-central moments of the rectified sEMG signal. Results: in 84% of the subjects, as contraction force increased from 0 to 10% MVC, the sEMG PDF shape oscillated back and forth between the semi-degenerate and the Gaussian distribution; the PDF–force relation varied greatly among subjects for forces between 0 and ~ 10% MVC, but this variability was largely reduced for forces above 10% MVC; the pooled analysis showed that, as contraction force gradually increased, the sEMG PDF evolved rapidly from the semi-degenerate towards the Laplacian distribution from 0 to 5% MVC, and then more slowly from the Laplacian towards the Gaussian distribution for higher forces. Conclusions: the study demonstrated that the dependence of the sEMG PDF shape on contraction force can only be reliably assessed by gradually increasing force from zero, and not by performing a few constant-force contractions. The study also showed that the PDF–force relation differed greatly among individuals for contraction forces below 10% MVC, but this variability was largely reduced when force increased above 10% MVC.
Open Access
The first and second phases of the muscle compound action potential in the thumb are differently affected by electrical stimulation trains
(American Physiological Society, 2024) Lanfranchi, Clément; Rodríguez Falces, Javier; Place, Nicolas; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC
Sarcolemmal membrane excitability is often evaluated by considering the peak-to-peak amplitude of the compound muscle action potential (M wave). However, the first and second M-wave phases represent distinct properties of the muscle action potential, which are differentially affected by sarcolemma properties and other factors such as muscle architecture. Contrasting with previous studies in which voluntary contractions have been used to induce muscle fatigue, we used repeated electrically induced tetanic contractions of the adductor pollicis muscle and assessed the kinetics of M-wave properties during the course of the contractions. Eighteen participants (24 ± 6 yr; means ± SD) underwent 30 electrically evoked tetanic contractions delivered at 30 Hz, each lasting 3 s with 1 s intervals. We recorded the amplitudes of the first and second M-wave phases for each stimulation. During the initial stimulation train, the first and second M-wave phases exhibited distinct kinetics. The first phase amplitude showed a rapid decrease to reach ~59% of its initial value (P < 0.001), whereas the second phase amplitude displayed an initial transient increase of ~19% (P ¼ 0.007). Within subsequent trains, both the first and second phase amplitudes consistently decreased as fatigue developed with a reduction during the last train reaching ~47% of its initial value (P < 0.001). Analyzing the first M wave of each stimulation train unveiled different kinetics for the first and second phases during the initial trains, but these distinctions disappeared as fatigue progressed. These findings underscore the interplay of factors affecting the M wave and emphasize the significance of separately scrutinizing its first and second phases when assessing membrane excitability adjustments during muscle contractions.
Open Access
Análisis del proceso de llenado de la señal sEMG a medida que aumenta gradualmente la fuerza en el cuádriceps
(Sociedad Española de Ingeniería Biomédica, 2024) Recalde Villamayor, Silvia; Navallas Irujo, Javier; Mariscal Aguilar, Cristina; Rodríguez Falces, Javier; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC
Objetivos: No existe una comprensión completa del modo en que la señal EMG de superficie se llena progresivamente de potenciales de unidad motora (MUP) a medida que aumenta la fuerza. Intentamos investigar este proceso de llenado de sEMG. Métodos: Se registraron señales EMG superficiales del cuádriceps de sujetos sanos a medida que la fuerza aumentaba gradualmente de 0 a 40% MVC. El proceso de llenado sEMG se analizó midiendo el factor de llenado EMG (calculado a partir de los momentos no centrales de la señal sEMG rectificada). Resultados: (1) Al aumentar gradualmente la fuerza, aparecieron uno o dos saltos bruscos prominentes en la amplitud del sEMG entre el 0 y el 10% de la fuerza MVC en los vastos lateral y medial. (2) Los saltos de amplitud se originaban cuando aparecían en la señal de sEMG unos pocos MUP de gran amplitud, que destacaban claramente de la actividad de sEMG anterior. (3) Cada vez que se producía un salto brusco en la amplitud del sEMG, se iniciaba una nueva fase de llenado del sEMG. Conclusiones: El proceso de llenado del sEMG tuvo una o dos etapas en los músculos vastos, estando el sEMG casi completamente lleno a fuerzas muy bajas (2-12% MVC). Importancia: El factor de llenado es una herramienta prometedora útil para analizar el proceso de llenado EMG.
Open Access
Masked least-squares averaging in processing of scanning-EMG recordings with multiple-discharges
(Springer, 2020) Corera Orzanco, Íñigo; Malanda Trigueros, Armando; Rodríguez Falces, Javier; Navallas Irujo, Javier; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
Removing artifacts from nearby motor units is one of the main objectives when processing scanning-EMG recordings. Methods such as median filtering or masked least-squares smoothing (MLSS) can be used to eliminate artifacts in recordings with just one discharge of the motor unit potential (MUP) at each location. However, more effective artifact removal can be achieved if several discharges per position are recorded. In this case, processing usually involves averaging the discharges available at each position and then applying a median filter in the spatial dimension. The main drawback of this approach is that the median filter tends to distort the signal waveform. In this paper, we present a new algorithm that operates on multiple discharges simultaneously and in the spatial dimension. We refer to this algorithm as the multi masked least-squares smoothing (MMLSS) algorithm: an extension of the MLSS algorithm for the case of multiple discharges. The algorithm is tested using simulated scanning-EMG signals in different recording conditions, i.e., at different levels of muscle contraction and for different numbers of discharges per position. Results demonstrate that the algorithm eliminates artifacts more effectively than any previously available method and does so without distorting the waveform of the signal.
Open Access
Correlation between discharge timings of pairs of motor units reveals the presence but not the proportion of common synaptic input to motor neurons
(American Physiological Society, 2017) Rodríguez Falces, Javier; Negro, Francesco; Farina, Dario; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
We investigated whether correlation measures derived from pairs of motor unit (MU) spike trains are reliable indicators of the degree of common synaptic input to motor neurons. Several 50-s isometric contractions of the biceps brachii muscle were performed at different target forces ranging from 10 to 30% of the maximal voluntary contraction relying on force feedback. Forty-eight pairs of MUs were examined at various force levels. Motor unit synchrony was assessed by cross-correlation analysis using three indexes: the output correlation as the peak of the cross-histogram (ρ) and the number of synchronous spikes per second (CIS) and per trigger (E). Individual analysis of MU pairs revealed that ρ, CIS, and E were most often positively associated with discharge rate (87, 85, and 76% of the MU pairs, respectively) and negatively with interspike interval variability (69, 65, and 62% of the MU pairs, respectively). Moreover, the behavior of synchronization indexes with discharge rate (and interspike interval variability) varied greatly among the MU pairs. These results were consistent with theoretical predictions, which showed that the output correlation between pairs of spike trains depends on the statistics of the input current and motor neuron intrinsic properties that differ for different motor neuron pairs. In conclusion, the synchronization between MU firing trains is necessarily caused by the (functional) common input to motor neurons, but it is not possible to infer the degree of shared common input to a pair of motor neurons on the basis of correlation measures of their output spike trains.
Open Access
Validation of the filling factor index to study the filling process of the sEMG signal in the quadriceps
(Elsevier, 2023) Rodríguez Falces, Javier; Malanda Trigueros, Armando; Mariscal Aguilar, Cristina; Niazi, Imran Khan; Navallas Irujo, Javier; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Introduction: The EMG filling factor is an index to quantify the degree to which an EMG signal has been filled. Here, we tested the validity of such index to analyse the EMG filling process as contraction force was slowly increased. Methods: Surface EMG signals were recorded from the quadriceps muscles of healthy subjects as force was gradually increased from 0 to 40% MVC. The sEMG filling process was analyzed by measuring the EMG filling factor (calculated from the non-central moments of the rectified sEMG). Results: (1) As force was gradually increased, one or two prominent abrupt jumps in sEMG amplitude appeared between 0 and 10% of MVC force in all the vastus lateralis and medialis. (2) The jumps in amplitude were originated when a few large-amplitude MUPs, clearly standing out from previous activity, appeared in the sEMG signal. (3) Every time an abrupt jump in sEMG amplitude occurred, a new stage of sEMG filling was initiated. (4) The sEMG was almost completely filled at 2–12% MVC. (5) The filling factor decreased significantly upon the occurrence of an sEMG amplitude jump, and increased as additional MUPs were added to the sEMG signal. (6) The filling factor curve was highly repeatable across repetitions. Conclusions: It has been validated that the filling factor is a useful, reliable tool to analyse the sEMG filling process. As force was gradually increased in the vastus muscles, the sEMG filling process occurred in one or two stages due to the presence of abrupt jumps in sEMG amplitude.
Open Access
EMG modeling
(InTechOpen, 2012) Rodríguez Falces, Javier; Navallas Irujo, Javier; Malanda Trigueros, Armando; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
The aim of this chapter is to describe the approaches used for modelling electromyographic (EMG) signals as well as the principles of electrical conduction within the muscle. Sections are organized into a progressive, step-by-step EMG modeling of structures of increasing complexity. First, the basis of the electrical conduction that allows for the propagation of the EMG signals within the muscle is presented. Second, the models used for describing the electrical activity generated by a single fibre described. The third section is devoted to modeling the organization of the motor unit and the generation of motor unit potentials. Based on models of the architectural organization of motor units and their activation and firing mechanisms, the last section focuses on modeling the electrical activity of a complete muscle as recorded at the surface.
Open Access
Métodos de procesamiento y análisis de señales electromiográficas
(Gobierno de Navarra, 2009) Gila Useros, Luis; Malanda Trigueros, Armando; Rodríguez Carreño, Ignacio; Rodríguez Falces, Javier; Navallas Irujo, Javier; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
La electromiografía clínica es una metodología de registro y análisis de la actividad bioeléctrica del músculo esquelético orientada al diagnóstico de las enfermedades neuromusculares. Las posibilidades de aplicación y el rendimiento diagnóstico de la electromiografía han evolucionado paralelamente al conocimiento de las propiedades de la energía eléctrica y al desarrollo de la tecnología eléctrica y electrónica. A mediados del siglo XX se introdujo el primer equipo comercial de electromiografía para uso médico basado en circuitos electrónicos analógicos. El desarrollo posterior de la tecnología digital ha permitido disponer de sistemas controlados por microprocesadores cada vez más fiables y potentes para captar, representar, almacenar, analizar y clasificar las señales mioeléctricas. Es esperable que el avance de las nuevas tecnologías de la información y la comunicación pueda conducir en un futuro próximo a la aplicación de desarrollos de inteligencia artificial que faciliten la clasificación automática de señales así como sistemas expertos de apoyo al diagnóstico electromiográfico.
Embargo
Modeling the extracellular potential generated by a muscle fiber as the output signal of a convolutional system
(American Physiological Society, 2024-09-01) Rodríguez Falces, Javier; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC
A central topic in Bioelectricity is the generation of the extracellular potential that results from the propagation of a transmembrane action potential along the muscle fiber. However, the way in which the extracellular potential is determined by the propagating action potential is difficult to describe, conceptualize, and visualize. Moreover, traditional quantitative approaches aimed at modeling extracellular potentials involve complex mathematical formulations, which do not allow students to visualize how the extracellular potential is generated around the active fiber. The present study is aimed at presenting a novel pedagogical approach to teaching the generation of extracellular potentials produced by muscle fibers based on the convolution operation. The effectiveness of this convolutional model was tested using a written exam and a satisfaction survey. Most students reported that a great advantage of this model was that it simplifies the problem by dividing it into three distinct components: 1) the input signal (associated with the action potential), 2) the impulse response (linked to the system formed by the fiber and the recording electrode), and 3) the output signal (the extracellular potential). Another key aspect of the present approach was that the input signal was represented by a sequence of electric dipoles, which allowed students to visualize the individual contribution of each dipole to the resulting extracellular potential. The results of the survey indicate that the combination of basic principles of electrical fields and intuitive graphical representations largely improves students' understanding of Bioelectricity concepts and enhances their motivation to complete their studies of biomedical engineering.