Non-invasive detection of hypoglycemic episodes in Type 1 diabetes using intelligent hybrid rough neural system

Ling, SH; San, PP; Lam, HK; Nguyen, HT

Non-invasive detection of hypoglycemic episodes in Type 1 diabetes using intelligent hybrid rough neural system

Ling, SH

San, PP Lam, HK Nguyen, HT

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, 2014, pp. 1238 - 1242
Issue Date:: 2014-01-01

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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	Lam, HK	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, 2014, pp. 1238 - 1242	en_US
dc.identifier.isbn	9781479914883	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37282
dc.identifier.uri	http://hdl.handle.net/10453/37283
dc.identifier.uri	http://hdl.handle.net/10453/37281
dc.identifier.uri	http://hdl.handle.net/10453/37280
dc.identifier.uri	http://hdl.handle.net/10453/37279
dc.identifier.uri	http://hdl.handle.net/10453/32664
dc.description.abstract	© 2014 IEEE. Insulin-dependent diabetes mellitus is classified as Type 1 diabetes and it can be further classified as immunemediated or idiopathic. Through the analysis of electrocar-diographic (ECG) signals of 15 children with T1DM, an effective hypoglycemia detection system, hybrid rough set based neural network (RNN) is developed by the use of physiological parameters of ECG signal. In order to detect the status of hypoglycemia, the feature of ECG of type 1 diabetics are extracted and classified according to corresponding glucose levels. In this technique, the applied physiological inputs are partitioned into predicted (certain) or random (uncertain) parts using defined lower and boundary of rough regions. In this way, the neural network is designed to deal only with the boundary region which mainly consists of a random part of applied input signal causing inaccurate modeling of the data set. A global training algorithm, hybrid particle swarm optimization with wavelet mutation (HPSOWM) is introduced for parameter optimization of proposed RNN. The experiment is carried out using real data collected at Department of Health, Government of Western Australia. It indicated that the proposed hybrid architecture is efficient for hypoglycemia detection by achieving better sensitivity and specificity with less number of design parameters.	en_US
dc.relation.ispartof	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014	en_US
dc.relation.isbasedon	10.1109/CEC.2014.6900229	en_US
dc.title	Non-invasive detection of hypoglycemic episodes in Type 1 diabetes using intelligent hybrid rough neural system	en_US
dc.type	Conference Proceeding
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:

© 2014 IEEE. Insulin-dependent diabetes mellitus is classified as Type 1 diabetes and it can be further classified as immunemediated or idiopathic. Through the analysis of electrocar-diographic (ECG) signals of 15 children with T1DM, an effective hypoglycemia detection system, hybrid rough set based neural network (RNN) is developed by the use of physiological parameters of ECG signal. In order to detect the status of hypoglycemia, the feature of ECG of type 1 diabetics are extracted and classified according to corresponding glucose levels. In this technique, the applied physiological inputs are partitioned into predicted (certain) or random (uncertain) parts using defined lower and boundary of rough regions. In this way, the neural network is designed to deal only with the boundary region which mainly consists of a random part of applied input signal causing inaccurate modeling of the data set. A global training algorithm, hybrid particle swarm optimization with wavelet mutation (HPSOWM) is introduced for parameter optimization of proposed RNN. The experiment is carried out using real data collected at Department of Health, Government of Western Australia. It indicated that the proposed hybrid architecture is efficient for hypoglycemia detection by achieving better sensitivity and specificity with less number of design parameters.

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http://hdl.handle.net/10453/32664