Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes

Ling, SH; San, PP; Nguyen, HT

Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes

Ling, SH

San, PP Nguyen, HT

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Publication Type:: Journal Article
Citation:: ISA Transactions, 2016, 64 pp. 440 - 446
Issue Date:: 2016-09-01

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Field	Value	Language
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	San, PP	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.available	2020-05-25T19:03:05Z
dc.date.issued	2016-09-01	en_US
dc.identifier.citation	ISA Transactions, 2016, 64 pp. 440 - 446	en_US
dc.identifier.issn	0019-0578	en_US
dc.identifier.uri	http://hdl.handle.net/10453/44117
dc.description.abstract	© 2016 ISA Hypoglycemia is a very common in type 1 diabetic persons and can occur at any age. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances. Because of that, an accurate continuing hypoglycemia monitoring system is a very important medical device for diabetic patients. In this paper, we proposed a non-invasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal. To enhance the detection accuracy, extreme learning machine (ELM) is developed to recognize the presence of hypoglycemia. A clinical study of 16 children with T1DM is given to illustrate the good performance of ELM.	en_US
dc.relation	http://purl.org/au-research/grants/nhmrc/1102286
dc.relation	http://purl.org/au-research/grants/nhmrc/APP1102286
dc.relation.ispartof	ISA Transactions	en_US
dc.relation.isbasedon	10.1016/j.isatra.2016.05.008	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Long QT Syndrome	en_US
dc.subject.mesh	Diabetes Mellitus, Type 1	en_US
dc.subject.mesh	Hypoglycemia	en_US
dc.subject.mesh	Blood Glucose	en_US
dc.subject.mesh	Electrocardiography	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Heart Rate	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.subject.mesh	Adolescent	en_US
dc.subject.mesh	Child	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Machine Learning	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes	en_US
dc.type	Journal Article
utslib.citation.volume	64	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0910 Manufacturing Engineering	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	64	en_US

Abstract:

© 2016 ISA Hypoglycemia is a very common in type 1 diabetic persons and can occur at any age. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances. Because of that, an accurate continuing hypoglycemia monitoring system is a very important medical device for diabetic patients. In this paper, we proposed a non-invasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal. To enhance the detection accuracy, extreme learning machine (ELM) is developed to recognize the presence of hypoglycemia. A clinical study of 16 children with T1DM is given to illustrate the good performance of ELM.

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http://hdl.handle.net/10453/44117