Intelligent detection of hypoglycemic episodes in children with Type 1 diabetes using adaptive neural-fuzzy inference system

San, P; Ling, SS; Nguyen, HT

Intelligent detection of hypoglycemic episodes in children with Type 1 diabetes using adaptive neural-fuzzy inference system

San, P Ling, SS Nguyen, HT

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, pp. 6325 - 6328
Issue Date:: 2012-01

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Field	Value	Language
dc.contributor.author	San, P	en_US
dc.contributor.author	Ling, SS	en_US
dc.contributor.author	Nguyen, HT	en_US
dc.contributor.editor	Khoo, MCK	en_US
dc.date	2012-08-28	en_US
dc.date.issued	2012-01	en_US
dc.identifier.citation	Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, pp. 6325 - 6328	en_US
dc.identifier.isbn	978-1-4577-1787-1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22404
dc.description.abstract	Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mel- litus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, an intelligent diagnostics system, using the hybrid approach of adaptive neural fuzzy inference system (ANFIS), is developed to recognize the presence of hypoglycemia. The proposed ANFIS is characterized by adaptive neural network capabilities and the fuzzy inference system. To optimize the membership functions and adaptive network parameters, a global learning optimization algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used. For clinical study, 15 children with Type 1 diabetes volunteered for an overnight study. All the real data sets are collected from the Department of Health, Government of Western Australia. Several experiments were conducted with 5 patients each, for a training set (184 data points), a validation set (192 data points) and a testing set (153 data points), which are randomly selected. The effectiveness of the proposed detection method is found to be satisfactory by giving better sensitivity, 79.09% and acceptable specificity, 51.82%.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society	en_US
dc.relation.ispartof	International Conference of the IEEE Engineering in Medicine and Biology Society	en_US
dc.relation.isbasedon	10.1109/EMBC.2012.6347440	en_US
dc.rights	© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	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	Heart Rate	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Fuzzy Logic	en_US
dc.subject.mesh	Child	en_US
dc.title	Intelligent detection of hypoglycemic episodes in children with Type 1 diabetes using adaptive neural-fuzzy inference system	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	San Diego, California, USA	en_US
utslib.for	0903 Biomedical Engineering	en_US
dc.location.activity	San Diego, California, 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.consider-herdc	true	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2012-08-28	en_US

Abstract:

Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mel- litus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, an intelligent diagnostics system, using the hybrid approach of adaptive neural fuzzy inference system (ANFIS), is developed to recognize the presence of hypoglycemia. The proposed ANFIS is characterized by adaptive neural network capabilities and the fuzzy inference system. To optimize the membership functions and adaptive network parameters, a global learning optimization algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used. For clinical study, 15 children with Type 1 diabetes volunteered for an overnight study. All the real data sets are collected from the Department of Health, Government of Western Australia. Several experiments were conducted with 5 patients each, for a training set (184 data points), a validation set (192 data points) and a testing set (153 data points), which are randomly selected. The effectiveness of the proposed detection method is found to be satisfactory by giving better sensitivity, 79.09% and acceptable specificity, 51.82%.

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http://hdl.handle.net/10453/22404