Hypoglycemia detection for insulin-dependent diabetes mellitus: Evolved fuzzy inference system approach

Ling, SH; San, PP; Nguyen, HT

Hypoglycemia detection for insulin-dependent diabetes mellitus: Evolved fuzzy inference system approach

Ling, SH

San, PP Nguyen, HT

Permalink

Publication Type:: Chapter
Citation:: Computational Intelligence and its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques, 2012, pp. 61 - 85
Issue Date:: 2012-01-01

Closed Access

	Filename	Description	Size
	COMPUTATIONAL_INTELLIGENCE_AND_ITS_APPLICATIONS_EV..._----_(Fuzzy_Logics_and_their_Applications).pdf	Published version	300.58 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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.issued	2012-01-01	en_US
dc.identifier.citation	Computational Intelligence and its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques, 2012, pp. 61 - 85	en_US
dc.identifier.isbn	9781848166912	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115675
dc.description.abstract	© 2012 by Imperial College Press. All rights reserved. Insulin-dependent diabetes mellitus is classified as Type 1 diabetes and it can be further classified as immune-mediated or idiopathic. Hypoglycemia is a common and serious side effect of insulin therapy in patients with Type 1 diabetes. In this chapter, we measure physiological parameters continuously to provide a non-invasive hypoglycemia detection for Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters of the electrocardiogram (ECG) signal, such as heart rate, corrected QT interval, change of heart rate and change of corrected QT interval, an evolved fuzzy inference model is developed for classification of hypoglycemia. To optimize the rules and membership functions of the fuzzy system, a hybrid particle swarm optimization with wavelet mutation (HPSOWM) is introduced. For the clinical study, 15 children with Type 1 diabetes are volunteered overnight. All the real data sets are collected from the Department of Health, Government of Western Australia and are randomly organized into a training set (10 patients) and testing set (5 patients). The results show that the evolved fuzzy inference system approach performs well in terms of sensitivity and specificity.	en_US
dc.relation.ispartof	Computational Intelligence and its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques	en_US
dc.relation.isbasedon	10.1142/9781848166929_0004	en_US
dc.title	Hypoglycemia detection for insulin-dependent diabetes mellitus: Evolved fuzzy inference system approach	en_US
dc.type	Chapter
utslib.for	090302 Biomechanical Engineering	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	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:

© 2012 by Imperial College Press. All rights reserved. Insulin-dependent diabetes mellitus is classified as Type 1 diabetes and it can be further classified as immune-mediated or idiopathic. Hypoglycemia is a common and serious side effect of insulin therapy in patients with Type 1 diabetes. In this chapter, we measure physiological parameters continuously to provide a non-invasive hypoglycemia detection for Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters of the electrocardiogram (ECG) signal, such as heart rate, corrected QT interval, change of heart rate and change of corrected QT interval, an evolved fuzzy inference model is developed for classification of hypoglycemia. To optimize the rules and membership functions of the fuzzy system, a hybrid particle swarm optimization with wavelet mutation (HPSOWM) is introduced. For the clinical study, 15 children with Type 1 diabetes are volunteered overnight. All the real data sets are collected from the Department of Health, Government of Western Australia and are randomly organized into a training set (10 patients) and testing set (5 patients). The results show that the evolved fuzzy inference system approach performs well in terms of sensitivity and specificity.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/115675