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Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines

Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. It is observed as changes in the heart rate in synchrony with the respiration. RSA has been hypothesized to be due to a combination of linear and nonlinear effects. The quantification of the latter, in turn, has been suggest...

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Autores principales: Morales, John, Borzée, Pascal, Testelmans, Dries, Buyse, Bertien, Van Huffel, Sabine, Varon, Carolina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901929/
https://www.ncbi.nlm.nih.gov/pubmed/33633586
http://dx.doi.org/10.3389/fphys.2021.623781
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author Morales, John
Borzée, Pascal
Testelmans, Dries
Buyse, Bertien
Van Huffel, Sabine
Varon, Carolina
author_facet Morales, John
Borzée, Pascal
Testelmans, Dries
Buyse, Bertien
Van Huffel, Sabine
Varon, Carolina
author_sort Morales, John
collection PubMed
description Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. It is observed as changes in the heart rate in synchrony with the respiration. RSA has been hypothesized to be due to a combination of linear and nonlinear effects. The quantification of the latter, in turn, has been suggested as a biomarker to improve the assessment of several conditions and diseases. In this study, a framework to quantify RSA using support vector machines is presented. The methods are based on multivariate autoregressive models, in which the present samples of the heart rate variability are predicted as combinations of past samples of the respiration. The selection and tuning of a kernel in these models allows to solve the regression problem taking into account only the linear components, or both the linear and the nonlinear ones. The methods are tested in simulated data as well as in a dataset of polysomnographic studies taken from 110 obstructive sleep apnea patients. In the simulation, the methods were able to capture the nonlinear components when a weak cardiorespiratory coupling occurs. When the coupling increases, the nonlinear part of the coupling is not detected and the interaction is found to be of linear nature. The trends observed in the application in real data show that, in the studied dataset, the proposed methods captured a more prominent linear interaction than the nonlinear one.
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spelling pubmed-79019292021-02-24 Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines Morales, John Borzée, Pascal Testelmans, Dries Buyse, Bertien Van Huffel, Sabine Varon, Carolina Front Physiol Physiology Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. It is observed as changes in the heart rate in synchrony with the respiration. RSA has been hypothesized to be due to a combination of linear and nonlinear effects. The quantification of the latter, in turn, has been suggested as a biomarker to improve the assessment of several conditions and diseases. In this study, a framework to quantify RSA using support vector machines is presented. The methods are based on multivariate autoregressive models, in which the present samples of the heart rate variability are predicted as combinations of past samples of the respiration. The selection and tuning of a kernel in these models allows to solve the regression problem taking into account only the linear components, or both the linear and the nonlinear ones. The methods are tested in simulated data as well as in a dataset of polysomnographic studies taken from 110 obstructive sleep apnea patients. In the simulation, the methods were able to capture the nonlinear components when a weak cardiorespiratory coupling occurs. When the coupling increases, the nonlinear part of the coupling is not detected and the interaction is found to be of linear nature. The trends observed in the application in real data show that, in the studied dataset, the proposed methods captured a more prominent linear interaction than the nonlinear one. Frontiers Media S.A. 2021-02-05 /pmc/articles/PMC7901929/ /pubmed/33633586 http://dx.doi.org/10.3389/fphys.2021.623781 Text en Copyright © 2021 Morales, Borzée, Testelmans, Buyse, Van Huffel and Varon. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Morales, John
Borzée, Pascal
Testelmans, Dries
Buyse, Bertien
Van Huffel, Sabine
Varon, Carolina
Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines
title Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines
title_full Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines
title_fullStr Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines
title_full_unstemmed Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines
title_short Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines
title_sort linear and non-linear quantification of the respiratory sinus arrhythmia using support vector machines
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901929/
https://www.ncbi.nlm.nih.gov/pubmed/33633586
http://dx.doi.org/10.3389/fphys.2021.623781
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