Hypoglycaemia detection using fuzzy inference system with intelligent optimiser

Lai, JCY; Leung, FHF; Ling, SH

Hypoglycaemia detection using fuzzy inference system with intelligent optimiser

Lai, JCY Leung, FHF Ling, SH

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Publication Type:: Journal Article
Citation:: Applied Soft Computing Journal, 2014, 20 pp. 54 - 65
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Lai, JCY	en_US
dc.contributor.author	Leung, FHF	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.date.available	2020-05-25T19:08:33Z
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Applied Soft Computing Journal, 2014, 20 pp. 54 - 65	en_US
dc.identifier.issn	1568-4946	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37286
dc.identifier.uri	http://hdl.handle.net/10453/37284
dc.identifier.uri	http://hdl.handle.net/10453/33128
dc.identifier.uri	http://hdl.handle.net/10453/37285
dc.description.abstract	Hypoglycaemia is a medical term for a body state with a low level of blood glucose. It is a common and serious side effect of insulin therapy in patients with diabetes. In this paper, we propose a system model to measure physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus (TIDM) patients. The resulting model is a fuzzy inference system (FIS). The heart rate (HR), corrected QT interval of the electrocardiogram (ECG) signal (QTc), change of HR, and change of QTc are used as the input of the FIS to detect the hypoglycaemic episodes. An intelligent optimiser is designed to optimise the FIS parameters that govern the membership functions and the fuzzy rules. The intelligent optimiser has an implementation framework that incorporates two wavelet mutated differential evolution optimisers to enhance the training performance. A multi-objective optimisation approach is used to perform the training of the FIS in order to meet the medical standards on sensitivity and specificity. Experiments with real data of 16 children (569 data points) with TIDM are studied in this paper. The data are randomly separated into a training set with 5 patients (l99 data points), a validation set with 5 patients (177 data points) and a testing set with 5 patients (193 data points). Experiment results show that the proposed FIS tuned by the proposed intelligent optimiser can offer good performance of classification. © 2014 Published by Elsevier B.V.	en_US
dc.relation.ispartof	Applied Soft Computing Journal	en_US
dc.relation.isbasedon	10.1016/j.asoc.2013.12.015	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Hypoglycaemia detection using fuzzy inference system with intelligent optimiser	en_US
dc.type	Journal Article
utslib.citation.volume	20	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0806 Information Systems	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	20	en_US

Abstract:

Hypoglycaemia is a medical term for a body state with a low level of blood glucose. It is a common and serious side effect of insulin therapy in patients with diabetes. In this paper, we propose a system model to measure physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus (TIDM) patients. The resulting model is a fuzzy inference system (FIS). The heart rate (HR), corrected QT interval of the electrocardiogram (ECG) signal (QTc), change of HR, and change of QTc are used as the input of the FIS to detect the hypoglycaemic episodes. An intelligent optimiser is designed to optimise the FIS parameters that govern the membership functions and the fuzzy rules. The intelligent optimiser has an implementation framework that incorporates two wavelet mutated differential evolution optimisers to enhance the training performance. A multi-objective optimisation approach is used to perform the training of the FIS in order to meet the medical standards on sensitivity and specificity. Experiments with real data of 16 children (569 data points) with TIDM are studied in this paper. The data are randomly separated into a training set with 5 patients (l99 data points), a validation set with 5 patients (177 data points) and a testing set with 5 patients (193 data points). Experiment results show that the proposed FIS tuned by the proposed intelligent optimiser can offer good performance of classification. © 2014 Published by Elsevier B.V.

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http://hdl.handle.net/10453/33128