A model for the relation between respiratory neural and mechanical outputs. I. Theory

Younes, Magdy; Riddle, Wayne A.

doi:10.1152/jappl.1981.51.4.963

Cited by 70 publications

(48 citation statements)

References 30 publications

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“…Although in this model, two equation sets were proposed to quantify the mechanical cost, RSM2 was discarded in [9] because it always led to an impulsive inspiratory pressure profile with an extremely small inspiratory duty cycle; this behavior had been also noted by other researchers [83,75]. However, in this study with the used optimization techniques was possible to adjust the model parameters in order to obtain a response with a realistic behavior (see Fig.…”

Section: Optimization Problemmentioning

confidence: 76%

“…This model presents an optimal control that adjustsV E and breathing pattern as a function of minimization of WOB and includes dynamic elements that relate neural activity to ventilatory mechanics [75]. The model discriminates between the mechanical work carried out during inspiration and expiration, so that not only adjustsV E but also the set of variables associated with the breathing pattern in terms of the minimum WOB.…”

Section: Model Descriptionmentioning

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: Optimization Problemmentioning

confidence: 76%

Section: Model Descriptionmentioning

confidence: 99%

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

Higuita

Mañanas

Sánchez

et al. 2016

IEEE Systems Journal

View full text Add to dashboard Cite

show abstract

“…For example, multiple combinations of rising and releasing phases may occur during inspiratory effort. [16][17][18] Second, variation in breathing frequency, ventilator triggering delay, or the occurrence of intrinsic PEEP may provoke patient-ventilator asynchrony, which may generate unexpected airway pressure waveforms, including LIRAP. 6 Third, 4 types of ICU ventilators equipped with user-selectable expiratory cycle criteria were used.…”

Section: Discussionmentioning

confidence: 99%

Simulation of Late Inspiratory Rise in Airway Pressure During Pressure Support Ventilation

Lin

et al. 2014

Respir Care

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BACKGROUND: Late inspiratory rise in airway pressure (LIRAP, P aw /⌬T) caused by inspiratory muscle relaxation or expiratory muscle contraction is frequently seen during pressure support ventilation (PSV), although the modulating factors are unknown. METHODS: We investigated the effects of respiratory mechanics (normal, obstructive, restrictive, or mixed), inspiratory effort (؊2, ؊8, or ؊15 cm H 2 O), flow cycle criteria (5-40% peak inspiratory flow), and duration of inspiratory muscle relaxation (0.18 -0.3 s) on LIRAP during PSV using a lung simulator and 4 types of ventilators. RESULTS: LIRAP occurred with all lung models when inspiratory effort was medium to high and duration of inspiratory muscle relaxation was short. The normal lung model was associated with the fastest LIRAP, whereas the obstructive lung model was associated with the slowest. Unless lung mechanics were normal or mixed, LIRAP was unlikely to occur when inspiratory effort was low. Different ventilators were also associated with differences in LIRAP speed. Except for within the restrictive lung model, changes in flow cycle level did not abolish LIRAP if inspiratory effort was medium to high. Increased duration of inspiratory relaxation also led to the elimination of LIRAP. Simulation of expiratory muscle contraction revealed that LIRAP occurred only when expiratory muscle contraction occurred sometime after the beginning of inspiration. CONCLUSIONS: Our simulation study reveals that both respiratory resistance and compliance may affect LIRAP. Except for under restrictive lung conditions, LIRAP is unlikely to be abolished by simply lowering flow cycle criteria when inspiratory effort is strong and relaxation time is rapid. LIRAP may be caused by expiratory muscle contraction when it occurs during inspiration. Key words: patient-ventilator asynchrony; pressure support ventilation; late inspiratory rise in airway pressure. [Respir Care 2015;60(2):201-209.

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“…To our knowledge, only a limited number of works have been focused on the modeling of realistic respiratory pressure and flow profiles. In [44] and [34], the influence of the relations between neural output and muscle mechanical performance on the generation of pressure and flow profiles is analyzed. This relation is mainly characterized by seven parameters defining the duration, peak instant and the curvature of the neural output waveform, which are different during the inspiratory and expiratory phases.…”

Section: Respiratory Model Adapted To the Newbornmentioning

confidence: 99%

Mathematical Modeling of Respiratory System Mechanics in the Newborn Lamb

et al. 2013

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In this paper, a mathematical model of the respiratory mechanics is used to reproduce experimental signal waveforms acquired from three newborn lambs. As the main challenge is to determine specific lamb parameters, a sensitivity analysis has been realized to find the most influent parameters, which are identified using an evolutionary algorithm. Results show a close match between experimental and simulated pressure and flow waveforms obtained during spontaneous ventilation and pleural pressure variations acquired during the application of positive pressure, since Root Mean Square Errors (RMSE) equal to 0.0119, 0.0052 and 0.0094. The identified parameters were discussed in light of previous knowledge of respiratory mechanics in the newborn.

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A model for the relation between respiratory neural and mechanical outputs. I. Theory

Cited by 70 publications

References 30 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

Simulation of Late Inspiratory Rise in Airway Pressure During Pressure Support Ventilation

Mathematical Modeling of Respiratory System Mechanics in the Newborn Lamb

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