Ventricular repolarization variability for hypoglycemia detection

Nuryani, N; Ling, S; Nguyen, HT

Ventricular repolarization variability for hypoglycemia detection

Nuryani, N Ling, S

Nguyen, HT

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2011, pp. 7961 - 7964
Issue Date:: 2011-01-01

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Field	Value	Language
dc.contributor.author	Nuryani, N	en_US
dc.contributor.author	Ling, S https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2011-01-01	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2011, pp. 7961 - 7964	en_US
dc.identifier.isbn	9781424441211	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19265
dc.description.abstract	Hypoglycemia is the most acute and common complication of Type 1 diabetes and is a limiting factor in a glycemic management of diabetes. In this paper, two main contributions are presented; firstly, ventricular repolarization variabilities are introduced for hypoglycemia detection, and secondly, a swarm-based support vector machine (SVM) algorithm with the inputs of the repolarization variabilities is developed to detect hypoglycemia. By using the algorithm and including several repolarization variabilities as inputs, the best hypoglycemia detection performance is found with sensitivity and specificity of 82.14% and 60.19%, respectively. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/IEMBS.2011.6091963	en_US
dc.subject.mesh	Heart Ventricles	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Hypoglycemia	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Ventricular Function	en_US
dc.subject.mesh	Support Vector Machines	en_US
dc.title	Ventricular repolarization variability for hypoglycemia detection	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
dc.location.activity	Boston, Massachusetts, USA	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	closed_access
pubs.publication-status	Published	en_US

Abstract:

Hypoglycemia is the most acute and common complication of Type 1 diabetes and is a limiting factor in a glycemic management of diabetes. In this paper, two main contributions are presented; firstly, ventricular repolarization variabilities are introduced for hypoglycemia detection, and secondly, a swarm-based support vector machine (SVM) algorithm with the inputs of the repolarization variabilities is developed to detect hypoglycemia. By using the algorithm and including several repolarization variabilities as inputs, the best hypoglycemia detection performance is found with sensitivity and specificity of 82.14% and 60.19%, respectively. © 2011 IEEE.

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http://hdl.handle.net/10453/19265