Show simple item record

dc.contributor.authorSerna Higuita, Leidy Yanet
dc.contributor.authorMañanas Villanueva, Miguel Ángel
dc.contributor.authorMarín Sánchez, Jesús
dc.contributor.authorHernández Valdivieso, Alher Mauricio
dc.contributor.authorBenito, S.
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2017-03-06T13:21:05Z
dc.date.available2019-01-08T01:30:40Z
dc.date.issued2016-11-01
dc.identifier.citationSerna, L., Mañanas, M.A., Marin, J., Hernández, A.M., Benito, S. Optimization techniques in respiratory control system models. "Applied soft computing", 1 Novembre 2016, vol. 48, p. 431-443.
dc.identifier.issn1568-4946
dc.identifier.urihttp://hdl.handle.net/2117/101972
dc.description.abstractOne 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.
dc.format.extent13 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica
dc.subject.lcshArtificial respiration
dc.subject.lcshRespirators (Medical equipment)
dc.subject.otherRespiratory control system
dc.subject.otherOptimal control
dc.subject.otherOptimization algorithms
dc.subject.otherMechanical work of breathing
dc.titleOptimization techniques in respiratory control system models
dc.typeArticle
dc.subject.lemacRespiradors (Aparell mèdic)
dc.subject.lemacRespiració artificial
dc.subject.lemacMonitoratge de pacients -- Aparells i accessoris
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOART - BIOsignal Analysis for Rehabilitation and Therapy
dc.identifier.doi10.1016/j.asoc.2016.07.033
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S1568494616303684
dc.rights.accessOpen Access
local.identifier.drac19528001
dc.description.versionPostprint (author's final draft)
local.citation.authorSerna, L.; Mañanas, M.A.; Marin, J.; Hernández, A.M.; Benito, S.
local.citation.publicationNameApplied soft computing
local.citation.volume48
local.citation.startingPage431
local.citation.endingPage443


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder