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Correlated Variability in the Breathing Pattern and End-Expiratory Lung Volumes in Conscious Humans

机译:有意识的人类呼吸模式和呼气末肺体积的相关变异性

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摘要

In order to characterize the variability and correlation properties of spontaneous breathing in humans, the breathing pattern of 16 seated healthy subjects was studied during 40 min of quiet breathing using opto-electronic plethysmography, a contactless technology that measures total and compartmental chest wall volumes without interfering with the subjects breathing. From these signals, tidal volume (VT), respiratory time (TTOT) and the other breathing pattern parameters were computed breath-by-breath together with the end-expiratory total and compartmental (pulmonary rib cage and abdomen) chest wall volume changes. The correlation properties of these variables were quantified by detrended fluctuation analysis, computing the scaling exponentα. VT, TTOT and the other breathing pattern variables showed α values between 0.60 (for minute ventilation) to 0.71 (for respiratory rate), all significantly lower than the ones obtained for end-expiratory volumes, that ranged between 1.05 (for rib cage) and 1.13 (for abdomen) with no significant differences between compartments. The much stronger long-range correlations of the end expiratory volumes were interpreted by a neuromechanical network model consisting of five neuron groups in the brain respiratory center coupled with the mechanical properties of the respiratory system modeled as a simple Kelvin body. The model-based α for VT is 0.57, similar to the experimental data. While the α for TTOT was slightly lower than the experimental values, the model correctly predicted α for end-expiratory lung volumes (1.045). In conclusion, we propose that the correlations in the timing and amplitude of the physiological variables originate from the brain with the exception of end-expiratory lung volume, which shows the strongest correlations largely due to the contribution of the viscoelastic properties of the tissues. This cycle-by-cycle variability may have a significant impact on the functioning of adherent cells in the respiratory system.
机译:为了表征人类自发性呼吸的变异性和相关性,使用光电体积描记法研究了16位就座健康受试者在安静呼吸40分钟期间的呼吸模式,该技术是一种无接触技术,可测量总胸腔和胸腔壁的体积而不会产生干扰随着受试者的呼吸。从这些信号中,按呼吸计算潮气量(VT),呼吸时间(TTOT)和其他呼吸模式参数,以及呼气末总和间隔室(肺肋骨和腹部)胸壁体积的变化。通过去趋势波动分析对这些变量的相关特性进行量化,计算比例指数α。 VT,TTOT和其他呼吸模式变量显示的α值介于0.60(对于分钟通气)至0.71(对于呼吸频率)之间,均显着低于从呼气末容积获得的α值,介于1.05(对于胸廓)和1.13(对于腹部),隔室之间无显着差异。呼气末容积的更强的远距离相关性由神经力学网络模型解释,该模型由大脑呼吸中心的五个神经元组组成,并以简单的开尔文体为模型,模拟了呼吸系统的机械特性。 VT的基于模型的α为0.57,与实验数据相似。尽管TTOT的α值略低于实验值,但该模型正确预测了呼气末肺体积的α(1.045)。总之,我们提出生理变量的时间和幅度的相关性源自大脑,除了呼气末肺容积外,这显示出最强的相关性,这在很大程度上归因于组织的粘弹性质的贡献。这种逐周期的变化可能会对呼吸系统中粘附细胞的功能产生重大影响。

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