Novel Modeling of Work of Breathing for Its Optimization During Increased Respiratory Efforts

Abstract

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 CO 2 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 opti- mization 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 CO 2 inhalation, results showed that an alternative model has a more appropriate cost function to minimize WOB from a physiological viewpoint according to experimental data.

A Novel Strategy to Fit and Validate Physiological Models: A Case Study of a Cardiorespiratory Model for Simulation of Incremental Aerobic Exercise

“…It is used in the model's respiratory controller and lung mechanics to represent the motion equation of the respiratory system. Its nominal value is 21.9 cmH 2 O/l and agrees with that reported in [20,53] for healthy adult subjects. However, other authors report lower values of 10, 8.55, and 8.52 cmH 2 O/l.…”

“…In [20], a nominal value of 150 cmH 2 O is proposed, and variations of around ±66% of this value are evaluated. Subsequent work has used a value of 50 cmH 2 O on fitting and validating such a model with healthy subjects [53]. Following the above, although the optimization procedure can identify a value near the nominal value, variations higher than 100% could be expected.…”

An Improved Dynamic Model for the Respiratory Response to Exercise

“…RS1 and RS3 used an optimization principle that allows adjusting f R by minimizing work of breathing. In this sense, the minimization of WOB has been extensively considered as a control criterion to adjust the breathing pattern (Yamashiro and Grodins, 1971 ; Poon et al, 1992 ; Serna Higuita et al, 2014 ; Serna et al, 2016 ). Moreover, recent formulations seek to describe how sensory information influences the dynamics of respiratory rhythm under the hypothesis that “respiratory rhythms arise from the interplay of central rhythm generation circuits, biomechanics and feedback from peripheral signaling pathways” (Butera et al, 1999a , b ; Diekman et al, 2017 ).…”

“…Many empirical and functional models have been proposed in the literature to describe numerous aspects of the respiratory system (Fincham and Tehrani, 1983 ; Butera et al, 1999a , b ; Cheng et al, 2010 ; Williamson, 2010 ; Tsai and Lee, 2011 ; Cheng and Khoo, 2012 ; Serna Higuita et al, 2014 ; Serna et al, 2016 ; Diekman et al, 2017 ). Due to the primary goal of this system is to regulate the CO 2 and O 2 in the brain and body tissues, ventilatory stimuli like exercise, hypoxia and hypercapnia are frequently used to evaluate the performance of such models.…”

Estado del Arte en Neurotecnologías para la Asistencia y la Rehabilitación en España: Tecnologías Auxiliares, Trasferencia Tecnológica y Aplicación Clínica

“…En el ámbito del sistema respiratorio y el desarrollo de herramientas para la ayuda en la ventilación mecánica en pacientes respiratorios, el grupo BIOART, en colaboración con el Grupo de Investigación en Bioinstrumentación e Ingeniería clínica (GIBIC) de la Universidad de Antioquia, Colombia, ha analizado e implementado modelos dinámicos del sistema respiratorio que permiten predecir el patrón ventilatorio de un sujeto ante diferentes demandas ventilatorias (Serna et al, 2014). Dichos modelos se basan en principios de optimización que permiten ajustar la ventilación en función de la minimización del trabajo mecánico respiratorio.…”