Using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology

• Published in: Journal of clinical monitoring and computing Vol. 37; no. 1; pp. 165 - 177
• Main Authors: Enevoldsen, Johannes, Simpson, Gavin L., Vistisen, Simon T.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2023; Springer Nature B.V
• Subjects: Anesthesiology; Blood pressure; Blood Pressure - physiology; Critical Care Medicine; Decomposition; Fluid Therapy; Health Sciences; Heart; Humans; Intensive; Lung; Medicine; Medicine & Public Health; Original Research; Physiological effects; Physiology; Pressure effects; Respiration, Artificial; Respiratory Physiological Phenomena; Statistics for Life Sciences; Time Factors; Time series; Waveforms; Signal processing; Central venous pressure; Mechanical ventilation; Statistical modelling; Hemodynamic monitoring
• ISSN: 1387-1307; 1573-2614; 1573-2614
• DOI: 10.1007/s10877-022-00873-7

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.