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
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
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.
Open Access
EMG filling analysis, a new method for the assessment of recruitment of motor units with needle EMG
(Elsevier, 2025-02-20) Mariscal Aguilar, Cristina; Navallas Irujo, Javier; Malanda Trigueros, Armando; Recalde Villamayor, Silvia; Rodríguez Falces, Javier; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC
Objectives: The progression of recruitment of motor unit potentials (MUPs) during increasing voluntary contraction can provide important information about the motor units (MUs) innervating a muscle. Here, we described a method to quantitate the recruitment level of the intramuscular electromyographic (iEMG) signal during an increasing force level. Methods: Concentric needle EMG signals were recorded from the tibialis anterior of healthy subjects as force was gradually increased from 0 to maximum force. The iEMG filling process was analyzed by measuring the EMG filling factor (FF), calculated from the mean rectified iEMG and the root mean square iEMG. Results: (1) The iEMG activity at low contraction forces was “discrete” (FF<0.3) for all participants. (2) The iEMG activity at maximal effort was “full” (FF>0.5) for 83 % of the participants, whereas it was “incompletely-reduced” (0.3
Open Access
Evaluation of the electromyography test for the analysis of the aerobic-anaerobic transition in elite cyclists during incremental exercise
(MDPI, 2019) Latasa Zudaire, Iban Alexander; Córdova Martínez, Alfredo; Quintana Ortí, Gregorio; Lavilla Oiz, Ana; Navallas Irujo, Javier; Rodríguez Falces, Javier; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
(1) Background: The aim of this study was to investigate the validity and reliability of surface electromyography (EMG) for automatic detection of the aerobic and anaerobic thresholds during an incremental continuous cycling test using 1 min exercise periods in elite cyclists. (2) Methods: Sixteen well-trained cyclists completed an incremental exercise test (25 W/1 min) to exhaustion. Surface bipolar EMG signals were recorded from the vastus lateralis, vastus medialis, biceps femoris, and gluteus maximus, and the root mean square (RMS) were assessed. The multi-segment linear regression method was used to calculate the first and second EMG thresholds (EMG(T1) and EMG(T2)). During the test, gas exchange data were collected to determine the first and second ventilatory thresholds (VT1 and VT2). (3) Results: Two breakpoints (thresholds) were identified in the RMS EMG vs. time curve for all muscles in 75% of participants. The two breakpoints, EMG(T1) and EMG(T2) , were detected at around 70%-80% and 90%-95% of VO2MAX, respectively. No significant differences were found between the means of VT(1 )and EMGT(1) for the vastii and biceps femoris muscles (p > 0.05). There were no significant differences between means of EMG(T2) and VT2 (p > 0.05). (4) Conclusions: It is concluded that the multi-segment linear regression algorithm is a valid non-invasive method for analyzing the aerobic-anaerobic transition during incremental tests with 1 min stage durations.
Open Access
Modelling fibrillation potentials: analysis of time parameters in the muscle intracellular action potential
(IEEE, 2007) Rodríguez Falces, Javier; Malanda Trigueros, Armando; Gila Useros, Luis; Rodríguez Carreño, Ignacio; Navallas Irujo, Javier; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
A single fibre action potential (SFAP) can be modelled as the convolution of a biolectrical source and a filter impulse response. In the Dimitrov-Dimitrova (D-D) convolutional model, the first temporal derivative of the intracellular action potential (IAP) is used as the source, and T spl is a time parameter related to the duration of the IAP waveform. Our work is centred on the relation between Tspl and the main spike duration (MSD), defined as the time interval between the first and third phases of the SFAP. We show that Tspl essentially determines the MSD parameter. As experimental data, we used fibrillation potentials (FPs) of two different muscles to study the D-D model. We found that T spl should have a certain statistical variability in order to explain the variability in the MSD of our FPs. In addition, we present a method to estimate the T spl values corresponding to a given SFAP from its measured MSD.