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

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  • Additional Information
    • Source:
      Publisher: Springer Country of Publication: Netherlands NLM ID: 9806357 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-2614 (Electronic) Linking ISSN: 13871307 NLM ISO Abbreviation: J Clin Monit Comput Subsets: MEDLINE
    • Publication Information:
      Publication: Amsterdam : Springer
      Original Publication: Dordrecht, The Netherlands ; Boston : Kluwer Academic Publishers, c1998-
    • Subject Terms:
      Respiratory Physiological Phenomena* ; Lung*; Humans ; Time Factors ; Blood Pressure/physiology ; Heart ; Respiration, Artificial ; Fluid Therapy
    • 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.
      (© 2022. The Author(s).)
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    • Contributed Indexing:
      Keywords: Central venous pressure; Hemodynamic monitoring; Mechanical ventilation; Signal processing; Statistical modelling
    • Publication Date:
      Date Created: 20220613 Date Completed: 20230123 Latest Revision: 20230328
    • Publication Date:
      20231215
    • Accession Number:
      PMC9852126
    • Accession Number:
      10.1007/s10877-022-00873-7
    • Accession Number:
      35695942