Cardiovascular response identification based on nonlinear support vector regression

Wang, L; Su, SW; Chan, GSH; Celler, BG; Cheng, TM; Savkin, AV

Cardiovascular response identification based on nonlinear support vector regression

Wang, L Su, SW

Chan, GSH Celler, BG Cheng, TM Savkin, AV

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Publication Type:: Chapter
Citation:: 2008, 25 CCIS pp. 202 - 213
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Wang, L	en_US
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.contributor.author	Chan, GSH	en_US
dc.contributor.author	Celler, BG	en_US
dc.contributor.author	Cheng, TM	en_US
dc.contributor.author	Savkin, AV	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	2008, 25 CCIS pp. 202 - 213	en_US
dc.identifier.isbn	3540922180	en_US
dc.identifier.isbn	9783540922186	en_US
dc.identifier.uri	http://hdl.handle.net/10453/7872
dc.description.abstract	This study experimentally investigates the relationships between central cardiovascular variables and oxygen uptake based on nonlinear analysis and modeling. Ten healthy subjects were studied using cycle-ergometry exercise tests with constant workloads ranging from 25 Watt to 125 Watt. Breath by breath gas exchange, heart rate, cardiac output, stroke volume and blood pressure were measured at each stage. The modeling results proved that the nonlinear modeling method (Support Vector Regression) outperforms traditional regression method (reducing Estimation Error between 59% and 80%, reducing Testing Error between 53% and 72%) and is the ideal approach in the modeling of physiological data, especially with small training data set. © 2008 Springer-Verlag.	en_US
dc.relation.isbasedon	10.1007/978-3-540-92219-3_15	en_US
dc.title	Cardiovascular response identification based on nonlinear support vector regression	en_US
dc.type	Chapter
utslib.citation.volume	25 CCIS	en_US
utslib.for	010203 Calculus of Variations, Systems Theory and Control Theory	en_US
utslib.for	091302 Automation and Control Engineering	en_US
utslib.for	090303 Biomedical Instrumentation	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	25 CCIS	en_US

Abstract:

This study experimentally investigates the relationships between central cardiovascular variables and oxygen uptake based on nonlinear analysis and modeling. Ten healthy subjects were studied using cycle-ergometry exercise tests with constant workloads ranging from 25 Watt to 125 Watt. Breath by breath gas exchange, heart rate, cardiac output, stroke volume and blood pressure were measured at each stage. The modeling results proved that the nonlinear modeling method (Support Vector Regression) outperforms traditional regression method (reducing Estimation Error between 59% and 80%, reducing Testing Error between 53% and 72%) and is the ideal approach in the modeling of physiological data, especially with small training data set. © 2008 Springer-Verlag.

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http://hdl.handle.net/10453/7872