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Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements
BACKGROUND: Cardiac output (CO) and systemic vascular resistance (SVR) are two important parameters of the cardiovascular system. The ability to measure these parameters continuously and noninvasively may assist in diagnosing and monitoring patients with suspected cardiovascular diseases, or other c...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3649882/ https://www.ncbi.nlm.nih.gov/pubmed/23452705 http://dx.doi.org/10.1186/1475-925X-12-19 |
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author | Lee, Qim Y Redmond, Stephen J Chan, Gregory SH Middleton, Paul M Steel, Elizabeth Malouf, Philip Critoph, Cristopher Flynn, Gordon O’Lone, Emma Lovell, Nigel H |
author_facet | Lee, Qim Y Redmond, Stephen J Chan, Gregory SH Middleton, Paul M Steel, Elizabeth Malouf, Philip Critoph, Cristopher Flynn, Gordon O’Lone, Emma Lovell, Nigel H |
author_sort | Lee, Qim Y |
collection | PubMed |
description | BACKGROUND: Cardiac output (CO) and systemic vascular resistance (SVR) are two important parameters of the cardiovascular system. The ability to measure these parameters continuously and noninvasively may assist in diagnosing and monitoring patients with suspected cardiovascular diseases, or other critical illnesses. In this study, a method is proposed to estimate both the CO and SVR of a heterogeneous cohort of intensive care unit patients (N=48). METHODS: Spectral and morphological features were extracted from the finger photoplethysmogram, and added to heart rate and mean arterial pressure as input features to a multivariate regression model to estimate CO and SVR. A stepwise feature search algorithm was employed to select statistically significant features. Leave-one-out cross validation was used to assess the generalized model performance. The degree of agreement between the estimation method and the gold standard was assessed using Bland-Altman analysis. RESULTS: The Bland-Altman bias ±precision (1.96 times standard deviation) for CO was -0.01 ±2.70 L min(-1) when only photoplethysmogram (PPG) features were used, and for SVR was -0.87 ±412 dyn.s.cm(-5) when only one PPG variability feature was used. CONCLUSIONS: These promising results indicate the feasibility of using the method described as a non-invasive preliminary diagnostic tool in supervised or unsupervised clinical settings. |
format | Online Article Text |
id | pubmed-3649882 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-36498822013-05-10 Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements Lee, Qim Y Redmond, Stephen J Chan, Gregory SH Middleton, Paul M Steel, Elizabeth Malouf, Philip Critoph, Cristopher Flynn, Gordon O’Lone, Emma Lovell, Nigel H Biomed Eng Online Research BACKGROUND: Cardiac output (CO) and systemic vascular resistance (SVR) are two important parameters of the cardiovascular system. The ability to measure these parameters continuously and noninvasively may assist in diagnosing and monitoring patients with suspected cardiovascular diseases, or other critical illnesses. In this study, a method is proposed to estimate both the CO and SVR of a heterogeneous cohort of intensive care unit patients (N=48). METHODS: Spectral and morphological features were extracted from the finger photoplethysmogram, and added to heart rate and mean arterial pressure as input features to a multivariate regression model to estimate CO and SVR. A stepwise feature search algorithm was employed to select statistically significant features. Leave-one-out cross validation was used to assess the generalized model performance. The degree of agreement between the estimation method and the gold standard was assessed using Bland-Altman analysis. RESULTS: The Bland-Altman bias ±precision (1.96 times standard deviation) for CO was -0.01 ±2.70 L min(-1) when only photoplethysmogram (PPG) features were used, and for SVR was -0.87 ±412 dyn.s.cm(-5) when only one PPG variability feature was used. CONCLUSIONS: These promising results indicate the feasibility of using the method described as a non-invasive preliminary diagnostic tool in supervised or unsupervised clinical settings. BioMed Central 2013-03-04 /pmc/articles/PMC3649882/ /pubmed/23452705 http://dx.doi.org/10.1186/1475-925X-12-19 Text en Copyright © 2013 Lee et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Lee, Qim Y Redmond, Stephen J Chan, Gregory SH Middleton, Paul M Steel, Elizabeth Malouf, Philip Critoph, Cristopher Flynn, Gordon O’Lone, Emma Lovell, Nigel H Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements |
title | Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements |
title_full | Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements |
title_fullStr | Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements |
title_full_unstemmed | Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements |
title_short | Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements |
title_sort | estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3649882/ https://www.ncbi.nlm.nih.gov/pubmed/23452705 http://dx.doi.org/10.1186/1475-925X-12-19 |
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