Modelado autorregresivo de señales electroencefalográficas para simuladores médicos

The recording of brain electrical activity has led to a greater understandingof different neurophysiological states, has even made possible thediagnosis of some neuronal disorders, hence the importance of characterizationand understanding of the different morphologies that mayhave electroencephalography signals (EEG). The mathematical modelingof biomedical signals facilitates the development of simulators that canbe useful as medical training tools on computers or mobile devices. Thispaper presents the autoregressive (AR) modeling and simulation ofEEG signals in different physiological states: seizures, resting with eyesopen and eyes closed, and also under the presence of some of the mostcommon artifacts: muscle, eye blinking, electrode “pop”, and 60-Hz.The performance of the models has been validated in the time domainusing the percentage of fitting (FIT), which was always above 70%, andin the frequency domain through energy of the characteristic frequencybands of the EEG. The modeling methodology, figures of simulatedsignals and the values of the parameters evaluated are presented. Thewide variety of EEG signals modeled allow the development of brainsignals simulators for training of medical personnel, and also for theanalysis and characterization of EEG signals.El registro de la actividad eléctrica cerebral ha permitido la comprensiónde diferentes estados neurofisiológicos, posibilitando el diagnóstico dealgunos trastornos neuronales, de aquí, la importancia de la caracterizacióny el conocimiento de las diferentes morfologías que pueden presentarlas señales de electroencefalografía (EEG). El modelado matemático deseñales biomédicas facilita el desarrollo de simuladores que puedenservir como herramienta de entrenamiento médico en computadoreso dispositivos móviles. Este artículo presenta el modelado paramétricoautorregresivo (AR) y la simulación de señales EEG en diferentes estadosfisiológicos, como: reposo con ojos abiertos y cerrados y crisis epilépticas,además bajo la presencia de algunos de los artefactos más comunes,como son: parpadeo, actividad muscular, electrodo “pop” y ruido 60Hz. Se valida el desempeño de los modelos en el dominio del tiempo através del porcentaje de ajuste FIT, el cual siempre estuvo por encimadel 70%, y en el dominio de la frecuencia a través de la energía en lasbandas de frecuencia características del EEG. Se presenta la metodologíade modelado, los gráficos de las señales simuladas y los valores de losparámetros evaluados. La amplia variedad de señales EEG modeladaspermitirá el desarrollo de simuladores de señales cerebrales para elentrenamiento del personal médico, e igualmente para el análisis y lacaracterización de las señales de electroencefalografía

Cambios en la mecánica ventilatoria debidos a variaciones de la PEEP y la presión soporte: estudio en sujetos sanos bajo ventilación mecánica no invasiva

Introducción. Por lo general, la mecánica ventilatoria se ha estimado en modo controlado con el uso de aproximaciones no adecuadas para ventilación espontánea.Objetivo. Medir los cambios de la mecánica ventilatoria ante variaciones de la presión positiva al final de la expiración (PEEP, por su sigla en inglés) y la presión soporte (PS) en ventilación mecánica no invasiva.Materiales y métodos. A través de una estrategia no invasiva, se estimó la mecánica ventilatoria bajo diferentes niveles de PEEP y PS. Para tal fin, se utilizó un simulador mecánico y se registró una base de datos de 14 sujetos sanos conectados de manera no invasiva a un ventilador mecánico.Resultados. Se obtuvieron valores medianos de resistencia y compliancia de 91.2[77.8-135.9]mL/cmH2O y 8.3[6.1-10.4]cmH2O/L/s para los 14 sujetos sanos con PEEP y PS de 0 cmH2O, respectivamente. En los incrementos de PEEP, los sujetos presentaron aumento estadísticamente significativo en la compliancia. Por el contrario, en el incremento de presión soporte, no se observaron cambios de ningún parámetro.Conclusiones. Se encontraron valores de compliancia y resistencia, acordes con los configurados en el simulador mecánico y coherentes con los reportados en la literatura en el caso de sujetos sanos. Esto resulta de gran utilidad al tomar decisiones en unidades de cuidados intensivos.Introduction: Traditionally, ventilatory mechanics has been delivered in controlled modes through the use of inappropriate approaches for spontaneous ventilation.Objective: To measure the changes of ventilatory mechanics caused by PEEP and pressure support (PS) variations in non-invasive mechanical ventilation.Materials and methods: The ventilatory mechanics was evaluated through a non-invasive strategy, under different PEEP and pressure support levels. For this purpose, a mechanical simulator was used, and a database of 14 healthy subjects non-invasively connected to a mechanical ventilator was recorded.Results: For the 14 healthy subjects under PEEP and pressure support conditions of 0 cmH2O, the median resistance and compliance values were 91.2 [77.8-135.9] mL/cmH2O and 8.3[6.1-10.4]cmH2O/L/s, respectively. PEEP compliance showed a statistically significant increase in all subjects. On the other hand, no changes in any of the parameters were observed regarding pressure support increase.Conclusions: The proposed technique allowed to find compliance and resistance values consistent with those set in the mechanical simulator, which, in turn, coincide with those reported in the literature for healthy subjects. This information is useful for decision-making in intensive care units.

Modeling of heart rate variability and respiratory muscle activity in organophosphate poisoned patients

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksWe propose an extended model of cardiovascular regulation to assess heart rate variability in patients poisoned with organophosphate during their treatment with mechanical ventilation. The model was modified to fit a population of 21 patients poisoned with organophosphorus compounds and undergoing mechanical ventilation. The extended model incorporated the respiratory muscle activity measured by surface electromyography for quantifying the vagal-sympathetic engagement during spontaneous breathing test. The order and structure of the parasympathetic and the sympathetic transfer function with respect to the original model were modified to a second-order system. In this extended model, the parameters related to the vagal-sympathetic response (corner frequency and constant gain) were correlated with respiratory muscle activity. When the diaphragm's contractions were stronger, the sympathetic corner frequency increased while the parasympathetic corner frequency and gain decreased. Thus, the proposed model could be useful to improve the ventilatory support and pharmacological treatment for patients poisoned with organophosphorus compounds considering the vagal-sympathetic response inferred from the respiratory muscle activityPeer ReviewedPostprint (author's final draft

Optimization techniques in respiratory control system models

One of the most complex physiological systems whose modeling is still an open study is the respiratory control system where different models have been proposed based on the criterion of minimizing the work of breathing (WOB). The aim of this study is twofold: to compare two known models of the respiratory control system which set the breathing pattern based on quantifying the respiratory work; and to assess the influence of using direct-search or evolutionary optimization algorithms on adjustment of model parameters. This study was carried out using experimental data from a group of healthy volunteers under CO2 incremental inhalation, which were used to adjust the model parameters and to evaluate how much the equations of WOB follow a real breathing pattern. This breathing pattern was characterized by the following variables: tidal volume, inspiratory and expiratory time duration and total minute ventilation. Different optimization algorithms were considered to determine the most appropriate model from physiological viewpoint. Algorithms were used for a double optimization: firstly, to minimize the WOB and secondly to adjust model parameters. The performance of optimization algorithms was also evaluated in terms of convergence rate, solution accuracy and precision. Results showed strong differences in the performance of optimization algorithms according to constraints and topological features of the function to be optimized. In breathing pattern optimization, the sequential quadratic programming technique (SQP) showed the best performance and convergence speed when respiratory work was low. In addition, SQP allowed to implement multiple non-linear constraints through mathematical expressions in the easiest way. Regarding parameter adjustment of the model to experimental data, the evolutionary strategy with covariance matrix and adaptation (CMA-ES) provided the best quality solutions with fast convergence and the best accuracy and precision in both models. CMAES reached the best adjustment because of its good performance on noise and multi-peaked fitness functions. Although one of the studied models has been much more commonly used to simulate respiratory response to CO2 inhalation, results showed that an alternative model has a more appropriate cost function to minimize WOB from a physiological viewpoint according to experimental data.Postprint (author's final draft

Changes in ventilatory mechanics caused by variations in PEEP and pressure support : study in healthy subjects under non-invasive mechanical ventilation

RESUMEN: Introducción. Por lo general, la mecánica ventilatoria se ha estimado en modo controlado con el uso de aproximaciones no adecuadas para ventilación espontánea. Objetivo. Medir los cambios de la mecánica ventilatoria ante variaciones de la presión positiva al final de la expiración (PEEP, por su sigla en inglés) y la presión soporte (PS) en ventilación mecánica no invasiva. Materiales y métodos. A través de una estrategia no invasiva, se estimó la mecánica ventilatoria bajo diferentes niveles de PEEP y PS. Para tal fin, se utilizó un simulador mecánico y se registró una base de datos de 14 sujetos sanos conectados de manera no invasiva a un ventilador mecánico. Resultados. Se obtuvieron valores medianos de resistencia y compliancia de 91.2[77.8-135.9]mL/cmH2O y 8.3[6.1-10.4]cmH2O/L/s para los 14 sujetos sanos con PEEP y PS de 0 cmH2O, respectivamente. En los incrementos de PEEP, los sujetos presentaron aumento estadísticamente significativo en la compliancia. Por el contrario, en el incremento de presión soporte, no se observaron cambios de ningún parámetro. Conclusiones. Se encontraron valores de compliancia y resistencia, acordes con los configurados en el simulador mecánico y coherentes con los reportados en la literatura en el caso de sujetos sanos. Esto resulta de gran utilidad al tomar decisiones en unidades de cuidados intensivos.ABSTRACT: Introduction: Traditionally, ventilatory mechanics has been delivered in controlled modes through the use of inappropriate approaches for spontaneous ventilation. Objective: To measure the changes of ventilatory mechanics caused by PEEP and pressure support (PS) variations in non-invasive mechanical ventilation. Materials and methods: The ventilatory mechanics was evaluated through a non-invasive strategy, under different PEEP and pressure support levels. For this purpose, a mechanical simulator was used, and a database of 14 healthy subjects non-invasively connected to a mechanical ventilator was recorded. Results: For the 14 healthy subjects under PEEP and pressure support conditions of 0 cmH2O, the median resistance and compliance values were 91.2 [77.8-135.9] mL/cmH2O and 8.3[6.1-10.4]cmH2O/L/s, respectively. PEEP compliance showed a statistically significant increase in all subjects. On the other hand, no changes in any of the parameters were observed regarding pressure support increase. Conclusions: The proposed technique allowed to find compliance and resistance values consistent with those set in the mechanical simulator, which, in turn, coincide with those reported in the literature for healthy subjects. This information is useful for decision-making in intensive care units

Laboratorio virtual para prácticas de control por computador

Aquest treball descriu un laboratori virtual desenvolupat per a donar suport a les pràctiques de Control per Computador de l'ETSEIB (Escola Tècnica Superior d'Enginyers Industrials de Barcelona). El laboratori està totalment desenvolupat mitjançant Easy Java Simulations (EJS).Peer Reviewe

An integrated mathematical model of the cardiovascular and respiratory response to exercise: Model-building and comparison with reported models

The use of physiological models in medicine allows the evaluation of new hypotheses, development of diagnosis and clinical treatment applications, and development of training and medical education tools, as well as medical device design. Although several mathematical models of physiological systems have been presented in the literature, few of them are able to predict the human cardiorespiratory response under physical exercise stimulus adequately. This paper aims to present the building and comparison of an integrated cardiorespiratory model focused on the prediction of the healthy human response under rest and aerobic exercise. The model comprises cardiovascular circulation, respiratory mechanics, and gas exchange system, as well as cardiovascular and respiratory controllers. Every system is based on previously reported physiological models and incorporates reported mechanisms related to the aerobic exercise dynamics. Experimental data of 30 healthy male volunteers undergoing a cardiopulmonary exercise test and simulated data from two of the most current and complete cardiorespiratory models were used to evaluate the performance of the presented model. Experimental design, processing, and exploratory analysis are described in detail. The simulation results were compared against the experimental data in steady state and in transient regime. The predictions of the proposed model closely mimic the experimental data, showing in overall the lowest prediction error (10.35%), the lowest settling times for cardiovascular and respiratory variables, and in general the fastest and similar responses in transient regime. These results suggest that the proposed model is suitable to predict the cardiorespiratory response of healthy adult humans under rest and aerobic exercise conditions.Peer ReviewedPostprint (published version

A novel strategy to fit and validate physiological models: a case study of acardiorespiratory model for simulation of incremental aerobic exercise

Applying complex mathematical models of physiological systems is challenging due to the large number of parameters. Identifying these parameters through experimentation is difficult, and although procedures for fitting and validating models are reported, no integrated strategy exists. Additionally, the complexity of optimization is generally neglected when the number of experimental observations is restricted, obtaining multiple solutions or results without physiological justification. This work proposes a fitting and validation strategy for physiological models with many parameters under various populations, stimuli, and experimental conditions. A cardiorespiratory system model is used as a case study, and the strategy, model, computational implementation, and data analysis are described. Using optimized parameter values, model simulations are compared to those obtained using nominal values, with experimental data as a reference. Overall, a reduction in prediction error is achieved compared to that reported for model building. Furthermore, the behavior and accuracy of all the predictions in the steady state were improved. The results validate the fitted model and provide evidence of the proposed strategy’s usefulness.Peer ReviewedPostprint (published version

Novel computational protocol to support transfemoral prosthetic alignment procedure using machine learning techniques

The prosthetic alignment procedure considers biomechanical, anatomical and comfort characteristics of the amputee to achieve an acceptable gait. Prosthetic malalignment induces long-term disease. The assessment of alignment is highly variable and subjective to the experience of the prosthetist, so the use of machine learning could assist the prosthetist during the judgment of optimal alignment.Peer ReviewedPostprint (published version

Successful object encoding induces increased directed connectivity in presymptomatic early-onset Alzheimer's disease

Background: Recent studies report increases in neural activity in brain regions critical to episodic memory at preclinical stages of Alzheimer’s disease (AD). Although electroencephalography (EEG) is widely used in AD studies, given its non-invasiveness and low cost, there is a need to translate the findings in other neuroimaging methods to EEG. Objective: To examine how the previous findings using functional magnetic resonance imaging (fMRI) at preclinical stage in presenilin-1 E280A mutation carriers could be assessed and extended, using EEG and a connectivity approach. Methods: EEG signals were acquired during resting and encoding in 30 normal cognitive young subjects, from an autosomal dominant early-onset AD kindred from Antioquia, Colombia. Regions of the brain previously reported as hyperactive were used for connectivity analysis. Results: Mutation carriers exhibited increasing connectivity at analyzed regions. Among them, the right precuneus exhibited the highest changes in connectivity. Conclusion: Increased connectivity in hyperactive cerebral regions is seen in individuals, genetically-determined to develop AD, at preclinical stage. The use of a connectivity approach and a widely available neuroimaging technique opens the possibility to increase the use of EEG in early detection of preclinical AD.Postprint (author's final draft