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
Exact inter-discharge interval distribution of motor unit firing patterns with gamma model
(Springer, 2019) Navallas Irujo, Javier; Porta Cuéllar, Sonia; Malanda Trigueros, Armando; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Inter-discharge interval distribution modeling of the motor unit firing pattern plays an important role in electromyographic decomposition and the statistical analysis of firing patterns. When modeling firing patterns obtained from automatic procedures, false positives and false negatives can be taken into account to enhance performance in estimating firing pattern statistics. Available models of this type, however, are only approximate and use Gaussian distributions, which are not strictly suitable for modeling renewal point processes. In this paper, the theory of point processes is used to derive an exact solution to the distribution when a gamma distribution is used to model the physiological firing pattern. Besides being exact, the solution provides a way to model the skewness of the inter-discharge distribution, and this may make it possible to obtain a better fit with available experimental data. In order to demonstrate potential applications of the model, we use it to obtain a maximum likelihood estimator of firing pattern statistics. Our tests found this estimator to be reliable over a wide range of firing conditions, whether dealing with real or simulated firing patterns, the proposed solution had better agreement than other models.
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
Advancing ASL kidney image registration: a tailored pipeline with VoxelMorph
(Springer, 2025-01-31) Oyarzun Domeño, Anne; Cia Alonso, Izaskun; Echeverría Chasco, Rebeca; Fernández Seara, María A.; Martín Moreno, Paloma L.; García Fernández, Nuria; Bastarrika, Gorka; Navallas Irujo, Javier; Villanueva Larre, Arantxa; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa; Gobierno de Navarra / Nafarroako Gobernua
In clinical renal assessment, image registration plays a pivotal role, as patient movement during data acquisition can significantly impede image post-processing and the accurate estimation of hemodynamic parameters. This study introduces a deep learning-based image registration framework specifically for arterial spin labeling (ASL) imaging. ASL is a magnetic resonance imaging technique that modifies the longitudinal magnetization of blood perfusing the kidney using a series of radiofrequency pulses combined with slice-selective gradients. After tagging the arterial blood, label images are captured following a delay, allowing the tagged blood bolus to enter the renal tissue, while control images are acquired without tagging the arterial spins. Given that perfusion maps are generated at the pixel level by subtracting control images from label images and considering the relatively small signal intensity difference, precise alignment of these images is crucial to minimize motion artefacts and prevent significant errors in perfusion calculations. Moreover, due to the extended ASL acquisition times and the anatomical location of the kidneys, renal images are often susceptible to pulsation, peristalsis, and breathing motion. These motion-induced noises and other instabilities can adversely affect ASL imaging outcomes, making image registration essential. However, research on renal MRI registration, particularly with respect to learning-based techniques, remains limited, with even less focus on renal ASL. Our study proposes a learning-based image registration approach that builds upon VoxelMorph and introduces groupwise inference as a key enhancement. The dataset includes 2448 images of transplanted kidneys (TK) and 2456 images of healthy kidneys (HK). We compared the automatic image registration results with the widely recognized optimization method Elastix. The model’s performance was evaluated using the mean structural similarity index (MSSIM), normalized correlation coefficient (NCC), temporal signal-tonoise ratio (TSNR) of the samples, and the mean cortical signal (CSIM) in perfusion-weighted images, thereby extending the evaluation beyond traditional similarity-based metrics. Our method achieved superior image registration performance, with peak NCC (0.987 ± 0.006) and MSSIM (0.869 ± 0.048) values in the kidney region, significantly surpassing Elastix and the unregistered series (p\ 0.05) on TK and HK datasets. Regularization analysis showed that higher k values (1, 2) produced smoother deformation fields, while moderate k values (0.5, 0.9) balanced smoothness and detail, maintaining low non-positive Jacobian percentages (\1%) comparable to Elastix. Additionally, our method improved CSIM by 14.3% (2.304 ± 1.167) and TSNR by 13.1% (3.888 ± 2.170) in TK, and achieved up to 13.2% (CSIM) and 29.8% (TSNR) enhancements in HK, demonstrating robustness and improved signal quality across datasets and acquisition techniques.
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