<i>Mechanics of Breathing in Man</i>

Otis, Arthur B.; Fenn, Wallace O.; Rahn, Hermann

doi:10.1152/jappl.1950.2.11.592

Cited by 825 publications

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“…The pattern displayed by humans during both conditions corresponds to that of the chemical but not the exercise response of quadrupeds. It has been claimed that the unique pattern of breathing observed in humans is the result of an optimization criterion which combines depth and frequency of breathing in order to attain the lowest energy expenditure within the relatively constant mechanical constraints of the respiratory system (Otis et al 1950;Mead, 1960;Priban & Fincham, 1965;Yamashiro et al 1975). It can be speculated that a number of benefits may derive from the integration of locomotion and respiration, since quadruped locomotion could affect the mechanical characteristics of the respiratory system.…”

Section: Discussionmentioning

confidence: 99%

“…At trot, a definite but rather variable and inconstant entrainment has also been observed in horses (Bramble 1989;Art, Desmecht, Amory & Lekeux, 1990;Lafortuna & Saibene, 1991 (Hey, Lloyd, Cunningham, Jukes & Bolton, 1966;Kay, Strange-Petersen & Vejby-Christensen, 1975b;Cunningham, Robbins and Wolff, 1986). This possibly results from respiratory control mechanisms geared to a principle of energy optimization (Otis, Fenn & Rahn, 1950;Mead, 1960;Priban & Fincham, 1965;Yamashiro, Dubenspeck, Lauritsen & Grodins, 1975 The expansion of this tube ranged from a minimum of 045 m to a maximum of 5.7 m. The minimum volume of the total VD thus obtained was 7-3 1 (fully compressed) and the maximum was 66-7 1 (fully extended). The added resistance was independent of the length of the tube and was on average 0 035 cmH2O F'-s-l at the airflow observed under the experimental conditions of the study.…”

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confidence: 96%

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The effects of locomotor‐respiratory coupling on the pattern of breathing in horses.

Lafortuna

Reinach

Saibene

1996

The Journal of Physiology

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1. To investigate the effect of locomotor activity on the pattern of breathing in quadrupeds, ventilatory response was studied in four healthy horses during horizontal and inclined (7 %) treadmill exercise at different velocities (1 4-6-9 m s-1) and during chemical stimulation with a rebreathing method. Stride frequency (fe) and locomotor-respiratory coupling (LRC)were also simultaneously determined by means of video recordings synchronized with respiratory events. 2. Tidal volume (VT) was positively correlated with pulmonary ventilation (VE) but significantly different linear regression equations were found between the experimental conditions (P < 0 0001), since the chemical hyperventilation was mainly due to increases in VT, whereas the major contribution to exercise hyperpnoea came from changes in respiratory frequency (fR).3. The average fR at each exercise level was not significantly different from f", although therewas not always a tight 1: 1 LRC. At constant speeds, fs was independent of the treadmill slope and hence the greater VE during inclined exercise was due to increased VT. 4. At any ventilatory level, the differences in breathing patterns between locomotion and rebreathing or locomotion at different slopes derived from different set points of the inspiratory off-switch mechanism. 5. The percentage of single breaths entrained with locomotor rhythm rose progressively and significantly with treadmill speed (P < 0 0001) up to a 1: 1 LRC and was significantly affected by treadmill slope (P < 0 001). 6. A LRC of 1: 1 was systematically observed at canter (10 out of 10 trials) and sometimes at trot (5 out of 14) and it entailed (i) a 4-to 5-fold reduction in both VT and fR variability, and (ii) a gait-specific phase locking of inspiratory onset during the locomotor cycle. 7. It is concluded that different patterns of breathing are employed during locomotion and rebreathing due to the interference between locomotor and respiratory functions, which may play a role in the optimization and control of exercise ventilation in horses.

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Section: Discussionmentioning

confidence: 99%

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confidence: 96%

The effects of locomotor‐respiratory coupling on the pattern of breathing in horses.

Lafortuna

Reinach

Saibene

1996

The Journal of Physiology

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“…The minimization of WOB has been extensively considered as a control criterion to adjust the breathing pattern [23,24,25,26,27,28,29]. Using this approach, several models have been proposed using minute ventilation as controlled variable.…”

Section: Respiratory Control System Modelingmentioning

confidence: 99%

“…Using this approach, several models have been proposed using minute ventilation as controlled variable. Early formulations were based on sinusoidal airflow patterns in which respiratory frequency was fitted in function of minimum work rate criteria [30,23] and minimum average driving pressure [31,32]. Optimal criteria were also developed to predict airflow patterns.…”

Section: Respiratory Control System Modelingmentioning

confidence: 99%

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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“…This restriction arises because the product of developed airway pressure (force per unit area) and the resulting delivered volume (area-length product) defines the energy cost of the breath to overcome resistance (R) and elastance (1/C). Inflation is subject to the energy conservation law and therefore is constrained mathematically by a relationship known as the equation of motion of the respiratory system [4]. The total inflation pressure (P tot ) corresponding to any volume (V) above the fully relaxed value (FRC) must be accounted for in the sum of dissipated and conserved pressures, usually approximated as: P tot = flow × R + V/C + PEEP tot .…”

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confidence: 99%