Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection

Nuryani, N; Ling, SSH; Nguyen, HT

Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection

Nuryani, N Ling, SSH

Nguyen, HT

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Publication Type:: Journal Article
Citation:: Annals of Biomedical Engineering, 2012, 40 (4), pp. 934 - 945
Issue Date:: 2012-04-01

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Field	Value	Language
dc.contributor.author	Nuryani, N	en_US
dc.contributor.author	Ling, SSH 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.available	2011-10-11	en_US
dc.date.issued	2012-04-01	en_US
dc.identifier.citation	Annals of Biomedical Engineering, 2012, 40 (4), pp. 934 - 945	en_US
dc.identifier.issn	0090-6964	en_US
dc.identifier.uri	http://hdl.handle.net/10453/18397
dc.description.abstract	Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity. © 2011 Biomedical Engineering Society.	en_US
dc.relation.ispartof	Annals of Biomedical Engineering	en_US
dc.relation.isbasedon	10.1007/s10439-011-0446-7	en_US
dc.subject.classification	Biomedical Engineering	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Diabetes Mellitus, Type 1	en_US
dc.subject.mesh	Hypoglycemia	en_US
dc.subject.mesh	Electrocardiography	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Arrhythmias, Cardiac	en_US
dc.title	Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	40	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	09 Engineering	en_US
utslib.for	11 Medical and Health Sciences	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.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	40	en_US

Abstract:

Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity. © 2011 Biomedical Engineering Society.

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http://hdl.handle.net/10453/18397