Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements
Open accessAbstract
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.
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Cardiac output has been estimated from pulse wave shape and low-frequency PPG variations.
“Cardiac output, monitored in peri-operative and critical care settings, has been estimated from pulse wave shape and low-frequency PPG variations”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [178]
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- Authors
- Lee, Q. Y., Redmond, S. J., Chan, G. S., Middleton, P. M., Steel, E., Malouf, P., Critoph, C., Flynn, G., O’Lone, E., & Lovell, N. H.
- Affiliations
- UNSW Sydney (AU)
- The University of Sydney (AU)
- Prince of Wales Hospital (AU)
- Corresponding author
- Lee, Qim Y
- Venue
- BioMedical Engineering OnLine
- Published
- 2013
- Volume
- 12
- Issue
- 1
- Pages
- 19
- ISSN
- 1475-925X (ISSN-L)
- PMC
- PMC3649882
- OpenAlex
- W2136022512
- Cited by
- 34
- References
- 27
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 1 · 2023: 1 · 2022: 6 · 2021: 1 · 2020: 1 · 2019: 4 · 2018: 2 · 2017: 6 · 2016: 2 · 2015: 4 · 2014: 1
- FWCI
- 1.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BioMedical Engineering OnLine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Surgery › Hemodynamic Monitoring and Therapy
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- cardiac output estimation, systemic vascular resistance, noninvasive hemodynamic monitoring, multivariate regression, feature selection, intensive care unit patients, mean arterial pressure, heart rate
- MeSH terms
- Aged, Blood Pressure, Cardiac Output, Cardiovascular Diseases, Cardiovascular System, Female, Fingers, Heart Rate, Humans, Male, Monitoring, Physiologic, Regression Analysis, Signal Processing, Computer-Assisted, Vascular Resistance, Reproducibility of Results, Multivariate Analysis, Photoplethysmography, Arterial Pressure
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