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

Johannes Enevoldsen; Gavin Simpson; Simon Tilma Vistisen

doi:10.1007/s10877-022-00873-7

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

Johannes Enevoldsen, Gavin Simpson, Simon Tilma Vistisen

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

2 Citationer (Scopus)

4 Downloads (Pure)

Abstract

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.

Originalsprog	Engelsk
Tidsskrift	Journal of Clinical Monitoring and Computing
Vol/bind	37
Nummer	1
Sider (fra-til)	165-177
Antal sider	13
ISSN	1387-1307
DOI	https://doi.org/10.1007/s10877-022-00873-7
Status	Udgivet - feb. 2023

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10.1007/s10877-022-00873-7Licens: CC BY

s10877-022-00873-7Forlagets udgivne version, 3,01 MBLicens: CC BY

Citationsformater

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abstract = "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{\textquoteright} 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.",

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Using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology. / Enevoldsen, Johannes ; Simpson, Gavin ; Vistisen, Simon Tilma.
I: Journal of Clinical Monitoring and Computing, Bind 37, Nr. 1, 02.2023, s. 165-177.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

TY - JOUR

T1 - Using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology

AU - Enevoldsen, Johannes

AU - Simpson, Gavin

AU - Vistisen, Simon Tilma

PY - 2023/2

Y1 - 2023/2

N2 - 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.

AB - 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.

KW - Central venous pressure

KW - Hemodynamic monitoring

KW - Mechanical ventilation

KW - Signal processing

KW - Statistical modelling

U2 - 10.1007/s10877-022-00873-7

DO - 10.1007/s10877-022-00873-7

M3 - Journal article

C2 - 35695942

SN - 1387-1307

VL - 37

SP - 165

EP - 177

JO - Journal of Clinical Monitoring and Computing

JF - Journal of Clinical Monitoring and Computing

IS - 1

ER -

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

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