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Use of intrinsic modes in biology: Examples of indicial response of pulmonary blood pressure to ± step hypoxia

机译:内在模式在生物学中的应用:肺部血压对±阶跃缺氧的局部反应实例

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摘要

Recently, a new method to analyze biological nonstationary stochastic variables has been presented. The method is especially suitable to analyze the variation of one biological variable with respect to changes of another variable. Here, it is illustrated by the change of the pulmonary blood pressure in response to a step change of oxygen concentration in the gas that an animal breathes. The pressure signal is resolved into the sum of a set of oscillatory intrinsic mode functions, which have zero “local mean,” and a final nonoscillatory mode. With this device, we obtain a set of “mean trends,” each of which represents a “mean” in a definitive sense, and together they represent the mean trend systematically with different degrees of oscillatory content. Correspondingly, the oscillatory content of the signal about any mean trend can be represented by a set of partial sums of intrinsic mode functions. When the concept of “indicial response function” is used to describe the change of one variable in response to a step change of another variable, we now have a set of indicial response functions of the mean trends and another set of indicial response functions to describe the energy or intensity of oscillations about each mean trend. Each of these can be represented by an analytic function whose coefficients can be determined by a least-squares curve-fitting procedure. In this way, experimental results are stated sharply by analytic functions.
机译:近来,提出了一种分析生物非平稳随机变量的新方法。该方法特别适合于分析一个生物变量相对于另一变量的变化的变化。在此,通过响应动物呼吸的气体中氧气浓度的阶跃变化来改变肺动脉血压来说明。压力信号被分解为一组振荡固有模式函数的总和,该函数具有零“局部均值”和最终的非振荡模式。使用该设备,我们获得了一组“平均趋势”,每个均值在确定的意义上表示一个“均值”,并且它们一起系统地表示具有不同程度振荡内容的平均趋势。相应地,关于任何平均趋势的信号振荡内容可以由一组固有模式函数的部分和表示。当使用“独立响应函数”的概念来描述一个变量响应另一个变量的阶跃变化时,我们现在有了一组平均趋势的独立响应函数,还有一组独立响应函数来描述每个平均趋势的振荡能量或强度。这些函数中的每一个都可以由一个解析函数表示,该函数的系数可以通过最小二乘曲线拟合程序确定。这样,通过分析函数可以清晰地说明实验结果。

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