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Using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology
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 c...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852126/ https://www.ncbi.nlm.nih.gov/pubmed/35695942 http://dx.doi.org/10.1007/s10877-022-00873-7 |
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author | Enevoldsen, Johannes Simpson, Gavin L. Vistisen, Simon T. |
author_facet | Enevoldsen, Johannes Simpson, Gavin L. Vistisen, Simon T. |
author_sort | Enevoldsen, Johannes |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10877-022-00873-7. |
format | Online Article Text |
id | pubmed-9852126 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-98521262023-01-21 Using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology Enevoldsen, Johannes Simpson, Gavin L. Vistisen, Simon T. J Clin Monit Comput Original Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10877-022-00873-7. Springer Netherlands 2022-06-13 2023 /pmc/articles/PMC9852126/ /pubmed/35695942 http://dx.doi.org/10.1007/s10877-022-00873-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Research Enevoldsen, Johannes Simpson, Gavin L. Vistisen, Simon T. Using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology |
title | Using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology |
title_full | Using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology |
title_fullStr | Using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology |
title_full_unstemmed | Using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology |
title_short | Using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology |
title_sort | using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852126/ https://www.ncbi.nlm.nih.gov/pubmed/35695942 http://dx.doi.org/10.1007/s10877-022-00873-7 |
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