Common physiological time series and waveforms are composed of repeating cardiac and respiratory cycles. Often, the cardiac effect is the primary interest, but for, e.g., fluid responsiveness prediction, the respiratory effect on arterial blood pressure also convey important information. In either case, it is relevant to disentangle the two effects. Generalized additive models (GAMs) allow estimating the effect of predictors as nonlinear, smooth functions. These smooth functions can represent the cardiac and respiratory cycles’ effects on a physiological signal. We demonstrate how GAMs allow a decomposition of physiological signals from mechanically ventilated subjects into separate effects of the cardiac and respiratory cycles. Two examples are presented. The first is a model of the respiratory variation in pulse pressure. The second demonstrates how a central venous pressure waveform can be decomposed into a cardiac effect, a respiratory effect and the interaction between the two cycles. Generalized additive models provide an intuitive and flexible approach to modelling the repeating, smooth, patterns common in medical monitoring data.

常见的生理时间序列和波形由重复的心脏和呼吸周期组成。通常，心脏效应是主要关注点，但对于例如液体反应性预测，呼吸对动脉血压的影响也传达了重要信息。在任何一种情况下，都需要区分这两种影响。广义加性模型 (GAM) 允许将预测变量的影响估计为非线性、平滑的函数。这些平滑函数可以表示心脏和呼吸周期对生理信号的影响。我们演示了 GAM 如何允许将来自机械通气受试者的生理信号分解为心脏和呼吸周期的独立影响。给出了两个例子。第一个是脉压呼吸变化模型。第二个演示了如何将中心静脉压波形分解为心脏效应、呼吸效应以及两个周期之间的相互作用。广义相加模型提供了一种直观且灵活的方法来对医疗监测数据中常见的重复、平滑的模式进行建模。