Advances in biomedical engineering: a call for enhancing empirical research

Advances in biomedical engineering (BME) imply the existence of research groups working in multidisciplinary teams to understand physiological processes and develop methods and tools for diagnostics and therapeutics. Multidisciplinary teams include physicians, biologists, physicists, mathematicians and engineers from different disciplines: electrical and electronics, computer sciences, materials, mechanical, chemical, among others. Lately, BME has become a bridge joining these disciplines. Therefore, successful BME projects involve not only a deep knowledge of the specific discipline, but also an understanding of the physiological phenomena under study.Advances in biomedical engineering (BME) imply the existence of research groups working in multidisciplinary teams to understand physiological processes and develop methods and tools for diagnostics and therapeutics. Multidisciplinary teams include physicians, biologists, physicists, mathematicians and engineers from different disciplines: electrical and electronics, computer sciences, materials, mechanical, chemical, among others. Lately, BME has become a bridge joining these disciplines. Therefore, successful BME projects involve not only a deep knowledge of the specific discipline, but also an understanding of the physiological phenomena under study

Feature extraction based on time-singularity multifractal spectrum distribution in intracardiac atrial fibrillation signals

El análisis de la dinámica no lineal de señales de Electrogramas Intracardiacos (EGM) ha sido propuesto como una herramienta para detectar sitios críticos de conducción eléctrica (ejm: rotores o múltiples frentes de onda) en fibrilación auricular (AF). Estudios previos han mostrado que el análisis multifractal puede ser de utilidad para detectar actividad crítica en la señal EGM. A pesar de esto, el análisis multifractal no considera la información temporal de la señal. Existe un nuevo formalismo matemático para superar esta limitación, el cual es llamado Distribución Tiempo-Singularidad del Espectro Multifractal (TS-MFSD), que involucra la variación en el tiempo del espectro. Este artículo describe una nueva metodología para calcular características a partir del TS-MFSD en señales EGM. Nosotros evaluamos los métodos descritos en una base de datos de EGM etiquetada por expertos en cuatro clases: no fragmentada, potenciales fragmentados discretos, actividad desorganizada y actividad continua. Para evaluar el rendimiento se calculó el área bajo la curva ROC. El mejor resultado de las características propuestas alcanzó un área bajo la curva ROC de 95.17% en la detección de señales con actividad continua. Este resultado supera los reportados mediante la utilización del análisis multifractal. Hasta donde sabemos, este es el primer trabajo que reporta la utilización de la TS-MFSD en señales biomédicas, y nuestros resultados sugieren que el análisis Tiempo-Singularidad tiene el potencial para estudiar el comportamiento no estacionario de las señales EGM en AF.Non-linear analysis of electrograms (EGM) has been proposed as a tool to detect critical conduction sites (e.g., rotors vortex, multiple wavefronts) in atrial fibrillation (AF). Likewise, studies have shown that multifractal analysis is useful to detect critical activity in EGM signals. However, the multifractal spectrum does not consider the temporal information. There is a new mathematical formalism to overcome this limitation: the time-singularity multifractal spectrum distribution (TS-MFSD), which involves the time variation of the spectrum. In this manuscript, we describe the methodology to compute the TS-MFSD from EGM signals. Moreover, we propose a methodology to extract features from time-singularity spectrum and from singularity energy spectrum (SES). We tested the features in an EGM database labeled by experts as: non-fragmented, discrete fragmented potentials, disorganized activity, and continuous activity. We tested the area under the receiver operating characteristic (ROC) curve. The proposed features achieve an area under the ROC curve of 95.17% when detecting signals with continuous activity. These results outperform those reported using multifractal analysis. To our knowledge, this is the first work that report the use of TS-MFSD in biomedical signals and our findings suggest that time-singularity has the potential to be used in the study of non-stationary behavior of EGM signals in AF

Identificación automática de perturbaciones en calidad de energía usando aprendizaje de máquina.

Actualmente, los eventos de calidad de potencia (PQ) se han estudiado dado su importancia para las industrias, en cuanto a la eficiencia y la vida útil de los elementos conectados a los sistemas eléctricos. Si las perturbaciones relacionadas con los eventos de PQ se clasifican (identifican) rápidamente y con una precisión confiable, los costos y las pérdidas generadas se reducirían. En este trabajo presentamos un enfoque basado en aprendizaje de máquina para la identificación automática de eventos PQ. Nuestra propuesta comprende las siguientes etapas: empleamos un espacio de representación de características basado en parámetros de tiempo y frecuencia. Además, utilizamos una técnica de análisis de relevancia supervisada, llamada Relieff, para resaltar la capacidad discriminante de las características consideradas. Luego, evaluamos el éxito de la clasificación de eventos PQ con diferentes clasificadores agregando diferentes niveles de ruido bajo un esquema de validación cruzada de 10 particiones. En este sentido, se genera una base de datos sintética basada en el estándar IEEE 1159, considerando 3000 señales y diez clases (300 muestras por clase). Los resultados obtenidos muestran un rendimiento de clasificación adecuado con clasificadores simples, cuadrático y k-NN, en comparación con las metodologías más avanzadas del estado del art

Identificación automática de perturbaciones en calidad de energía usando aprendizaje de máquina.

Actualmente, los eventos de calidad de potencia (PQ) se han estudiado dado su importancia para las industrias, en cuanto a la eficiencia y la vida útil de los elementos conectados a los sistemas eléctricos. Si las perturbaciones relacionadas con los eventos de PQ se clasifican (identifican) rápidamente y con una precisión confiable, los costos y las pérdidas generadas se reducirían. En este trabajo presentamos un enfoque basado en aprendizaje de máquina para la identificación automática de eventos PQ. Nuestra propuesta comprende las siguientes etapas: empleamos un espacio de representación de características basado en parámetros de tiempo y frecuencia. Además, utilizamos una técnica de análisis de relevancia supervisada, llamada Relieff, para resaltar la capacidad discriminante de las características consideradas. Luego, evaluamos el éxito de la clasificación de eventos PQ con diferentes clasificadores agregando diferentes niveles de ruido bajo un esquema de validación cruzada de 10 particiones. En este sentido, se genera una base de datos sintética basada en el estándar IEEE 1159, considerando 3000 señales y diez clases (300 muestras por clase). Los resultados obtenidos muestran un rendimiento de clasificación adecuado con clasificadores simples, cuadrático y k-NN, en comparación con las metodologías más avanzadas del estado del art

Complexity of Atrial Fibrillation Electrograms Through Nonlinear Signal Analysis: In Silico Approach

Identification of atrial fibrillation (AF) mechanisms could improve the rate of ablation success. However, the incomplete understanding of those mechanisms makes difficult the decision of targeting sites for ablation. This work is focused on the importance of EGM analysis for detecting and modulating rotors to guide ablation procedures and improve its outcomes. Virtual atrial models are used to show how nonlinear measures can be used to generate electroanatomical maps to detect critical sites in AF. A description of the atrial cell mathematical models, and the procedure of coupling them within two‐dimensional and three‐dimensional virtual atrial models in order to simulate arrhythmogenic mechanisms, is given. Mathematical modeling of unipolar and bipolar electrogramas (EGM) is introduced. It follows a discussion of EGM signal processing. Nonlinear descriptors, such as approximate entropy and multifractal analysis, are used to study the dynamical behavior of EGM signals, which are not well described by a linear law. Our results evince that nonlinear analysis of EGM can provide information about the dynamics of rotors and other mechanisms of AF. Furthermore, these fibrillatory patterns can be simulated using virtual models. The combination of features using machine learning tools can be used for identifying arrhythmogenic sources of AF

Evolución del procesamiento natural del lenguaje

Written language has not been immune to cultural and technological changes. For example, the invention of the printing press in the 15th century, or the development of personal computers and smartphones in recent decades, were milestones that dramatically marked its evolution. In parallel with the development of language, the evolution of computing and the emergence of intelligent algorithms capable of making decisions have made impressive advances in recent years, forming a new area of study known as artificial intelligence (AI), considered by many as the next great revolution. The integration of AI techniques to interpret, manipulate and understand human language results in the branch of natural language processing (NLP), which, taking into account the latest advances in language generation models, can become the next great milestone in written language. The first NLP algorithms were rule-based, but later supervised classification schemes based on models such as logistic regression, support vector machines, hidden Markov models, or conditional random trees began to be used. The problem with traditional classification models is that they are oriented to evaluate each word, or the relationships between each word and the previous word, but they do not capture the context of the words in a complete sentence.El lenguaje escrito no ha sido ajeno a los cambios culturales y tecnológicos. Por ejemplo, la invención de la imprenta en el siglo XV, o el desarrollo de los computadores personales y los teléfonos inteligentes en las últimas décadas, fueron hitos que marcaron drásticamente su evolución. De manera paralela al desarrollo del lenguaje, la evolución de la computación y el surgimiento de algoritmos inteligentes, capaces de tomar decisiones han tenido avances impresionantes en los últimos años formando una nueva área de estudio conocida como inteligencia artificial IA, considerada por muchos como la siguiente gran revolución.   La integración de las técnicas de IA con el fin de interpretar, manipular y comprender el lenguaje humano da como resultado la rama del procesamiento natural del lenguaje PNL, la cual, teniendo en cuenta los últimos avances en modelos de generación de lenguaje, se puede convertir en el próximo gran hito en cuanto al lenguaje escrito. Los primeros algoritmos de PNL estaban basados en reglas, posteriormente se empezó a utilizar esquemas de clasificación supervisada basados en modelos como la regresión logística, las máquinas de soporte vectorial, los modelos ocultos de Markov, o los árboles aleatorios condicionales, entre otros. El problema de los modelos de clasificación tradicionales es que los mismos están orientados a evaluar cada palabra, o las relaciones entre cada palabra y la palabra anterior, pero no capturan el contexto de las mismas en una frase completa

Named Entity Recognition in Electronic Health Records: A Methodological Review

Objectives A substantial portion of the data contained in Electronic Health Records (EHR) is unstructured, often appearing as free text. This format restricts its potential utility in clinical decision-making. Named entity recognition (NER) methods address the challenge of extracting pertinent information from unstructured text. The aim of this study was to outline the current NER methods and trace their evolution from 2011 to 2022. Methods We conducted a methodological literature review of NER methods, with a focus on distinguishing the classification models, the types of tagging systems, and the languages employed in various corpora. Results Several methods have been documented for automatically extracting relevant information from EHRs using natural language processing techniques such as NER and relation extraction (RE). These methods can automatically extract concepts, events, attributes, and other data, as well as the relationships between them. Most NER studies conducted thus far have utilized corpora in English or Chinese. Additionally, the bidirectional encoder representation from transformers using the BIO tagging system architecture is the most frequently reported classification scheme. We discovered a limited number of papers on the implementation of NER or RE tasks in EHRs within a specific clinical domain. Conclusions EHRs play a pivotal role in gathering clinical information and could serve as the primary source for automated clinical decision support systems. However, the creation of new corpora from EHRs in specific clinical domains is essential to facilitate the swift development of NER and RE models applied to EHRs for use in clinical practice

Técnicas de Adquisición y Procesamiento de Señales Electrocardiográficas en la Detección de Arritmias Cardíacas

The development of ambulatory monitoring systems and its electrocardiographic (ECG) signal processing techniques has become an important field of investigation, due to its relevance in the early detection of cardiovascular diseases such as the arrhythmias. The current trend of this technology is oriented to the use of portable equipment and mobile devices such as Smartphones, which have been widely accepted due to the technical characteristics and common integration in daily life. A fundamental characteristic of these systems is their ability to reduce the most common types of noise by means of digital signal processing techniques.  Among the most used techniques are the adaptive filters and the Discrete Wavelet Transform (DWT) which have been successfully implemented in several studies. There are systems that integrate classification stages based on artificial intelligence, which increases the performance in the process of arrhythmias detection. These techniques are not only evaluated for their functionality but for their computational cost, since they will be used in real-time applications, and implemented in embedded systems. This paper shows a review of each of the stages in the construction of a standard ambulatory monitoring system, for the contextualization of the reader in this type of technology.El desarrollo de sistemas de  monitoreo  ambulatorio  y  sus  técnicas  de  procesamiento  de  la  señal  electrocardiográfica (ECG) se han convertido en un importante campo de investigación, debido a su relevancia en la detección temprana de enfermedades cardiovasculares, tales como arritmias. La tendencia actual de esta tecnología está orientada al uso de equipos portátiles y dispositivos móviles como los Smartphones, que han sido ampliamente aceptados debido a sus características técnicas y a su integración, cada vez más común, en la vida diaria. Una característica fundamental de estos sistemas es su capacidad de reducir los tipos más comunes de ruido mediante técnicas de procesamiento de señales digitales. Entre las técnicas más utilizadas se encuentran los filtros adaptativos y la Transformada Discreta Wavelet (DWT, por sus siglas en inglés), los cuales han sido implementados exitosamente en diversos estudios. Así mismo, se reportan sistemas que integran etapas de clasificación basadas en inteligencia artificial, con lo cual se aumenta el rendimiento en el proceso de detección de arritmias. En este sentido, estas técnicas no solo son evaluadas por su funcionalidad, sino por su costo computacional, debido a que deben ser utilizadas en aplicaciones en tiempo real, e implementadas en sistemas embebidos. Este documento presenta una revisión del estado del arte de cada una de las etapas en la construcción de un sistema de monitoreo ambulatorio estándar, para la contextualización del lector en este tipo de tecnologías

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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Directed Functional Coordination Analysis of Swallowing Muscles in Healthy and Dysphagic Subjects by Surface Electromyography

[EN] Swallowing is a complex sequence of highly regulated and coordinated skeletal and smooth muscle activity. Previous studies have attempted to determine the temporal relationship between the muscles to establish the activation sequence pattern, assessing functional muscle coordination with cross-correlation or coherence, which is seriously impaired by volume conduction. In the present work, we used conditional Granger causality from surface electromyography signals to analyse the directed functional coordination between different swallowing muscles in both healthy and dysphagic subjects ingesting saliva, water, and yoghurt boluses. In healthy individuals, both bilateral and ipsilateral muscles showed higher coupling strength than contralateral muscles. We also found a dominant downward direction in ipsilateral supra and infrahyoid muscles. In dysphagic subjects, we found a significantly higher right-to-left infrahyoid, right ipsilateral infra-to-suprahyoid, and left ipsilateral supra-to-infrahyoid interactions, in addition to significant differences in the left ipsilateral muscles between bolus types. Our results suggest that the functional coordination analysis of swallowing muscles contains relevant information on the swallowing process and possible dysfunctions associated with dysphagia, indicating that it could potentially be used to assess the progress of the disease or the effectiveness of rehabilitation therapies.This work was partially supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR), and the Regional Joint Call for R+D+i projects (G8+1, Medellin, Colombia; Research Project PE2020-9 (G8 2020-39651)).Ye-Lin, Y.; Prats-Boluda, G.; Galiano-Botella, M.; Roldan-Vasco, S.; Orozco-Duque, A.; Garcia-Casado, J. (2022). Directed Functional Coordination Analysis of Swallowing Muscles in Healthy and Dysphagic Subjects by Surface Electromyography. Sensors. 22(12):1-16. https://doi.org/10.3390/s22124513116221