Optimization character of inspiratory neural drive

Poon, Chi‐Sang; Lin, Shyan-Lung; Knudson, O. B.

doi:10.1152/jappl.1992.72.5.2005

Cited by 59 publications

(73 citation statements)

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“…Models of overall control of the breathing pattern using a two-level optimization problem have been also presented in [34,35,9].Particularly, Poon et al [9] proposed a mathematical model of respiratory control system based on minimization of WOB which includes mechanical and chemical costs of breathing and allows to optimize simultaneously ventilation and breathing pattern (detailed information is presented in Section 4). This model has been used in studies related to respiratory modeling not only by the same author [28,36,37] but also by others [38,39,40].…”

Section: Respiratory Control System Modelingmentioning

confidence: 99%

“…The aim of this study is twofold: firstly, to compare two known mechanical cost functions to quantify the WOB [9] and, secondly, to assess the influence of several known optimization techniques, such as direct search and evolutionary algorithms [10,11,12,13,14,15,16,17,18,19], on the adjustment of the breathing pattern (by the controller) and the model parameters (in the identification process).…”

Section: Introductionmentioning

confidence: 99%

“…Two nested optimizations were carried out for this purpose: the first one in the controller that minimizes the WOB by using both mechanical cost estimations proposed in [9] and the second one in the search of the model parameters associated with the response that better matched with experimental data. These data were recorded from a group of subjects under different levels of ventilation (V E ) produced by hypercapnic stimulation.…”

Section: Introductionmentioning

confidence: 99%

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

Higuita

Mañanas

Sánchez

et al. 2016

IEEE Systems Journal

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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.

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Section: Respiratory Control System Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Higuita

Mañanas

Sánchez

et al. 2016

IEEE Systems Journal

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“…The mathematical descriptions of these functional blocks depicted in Fig. 1 have been detailed in earlier reports [9][10][11] and are outlined below Eqs. (1)~ (7):…”

Section: Optimal Respiratory Control Modelmentioning

confidence: 99%

“…The hypothesis was later extended to model the integrative control of ventilatory responses and respiratory patterns, by expressing respiratory work rate of breathing in terms of the isometric respiratory driving pressure (instead of ventilation) [9]. Based on the optimal chemicalmechanical respiratory control model [8][9], a simulation platform initially implemented under MATLAB [10], was later revised using LabVIEW [11], to provide a realtime simulation tool for respiratory control, and a signal monitoring platform for instantaneous profiles and breathing pattern.…”

Section: Introductionmentioning

confidence: 99%

The Effect of mechanical resistive loading on optimal respiratory signals and breathing patterns under added dead space and CO2 breathing

Lin

Chang

2016

MATEC Web Conf.

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Abstract. Current study aims to investigate how the respiratory resistive loading affects the behaviour of the optimal chemical-mechanical respiratory control model, the respiratory signals and breathing pattern are optimized under external dead space loading and CO2 breathing. The respiratory control was modelled to include a neuro-muscular drive as the control output to derive the waveshapes of instantaneous airflow, lung volume profiles, and breathing pattern, including total/alveolar ventilation, breathing frequency, tidal volume, inspiratory/expiratory duration, duty cycle, and arterial CO2 pressure. The simulations were performed under various respiratory resistive loads, including no load, inspiratory resistive load, expiratory resistive load, and continuous resistive load. The dead space measurement was described with Gray's derivation, and simulation results were studied and compared with experimental findings.

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Computational Models and Emergent Properties of Respiratory Neural Networks

Lindsey

Rybak

Smith

2012

Comprehensive Physiology

106

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Computational models of the neural control system for breathing in mammals provide a theoretical and computational framework bringing together experimental data obtained from different animal preparations under various experimental conditions. Many of these models were developed in parallel and iteratively with experimental studies and provided predictions guiding new experiments. This data-driven modeling approach has advanced our understanding of respiratory network architecture and neural mechanisms underlying generation of the respiratory rhythm and pattern, including their functional reorganization under different physiological conditions. Models reviewed here vary in neurobiological details and computational complexity and span multiple spatiotemporal scales of respiratory control mechanisms. Recent models describe interacting populations of respiratory neurons spatially distributed within the Bötzinger and pre-Bötzinger complexes and rostral ventrolateral medulla that contain core circuits of the respiratory central pattern generator (CPG). Network interactions within these circuits along with intrinsic rhythmogenic properties of neurons form a hierarchy of multiple rhythm generation mechanisms. The functional expression of these mechanisms is controlled by input drives from other brainstem components, including the retrotrapezoid nucleus and pons, which regulate the dynamic behavior of the core circuitry. The emerging view is that the brainstem respiratory network has rhythmogenic capabilities at multiple levels of circuit organization. This allows flexible, state-dependent expression of different neural pattern-generation mechanisms under various physiological conditions, enabling a wide repertoire of respiratory behaviors. Some models consider control of the respiratory CPG by pulmonary feedback and network reconfiguration during defensive behaviors such as cough. Future directions in modeling of the respiratory CPG are considered.

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Optimization character of inspiratory neural drive

Cited by 59 publications

References 0 publications

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

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

The Effect of mechanical resistive loading on optimal respiratory signals and breathing patterns under added dead space and CO2 breathing

Computational Models and Emergent Properties of Respiratory Neural Networks

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