Genetic-algorithm-based multiple regression with fuzzy inference system for detection of nocturnal hypoglycemic episodes

Ling, SSH; Nguyen, HT

Genetic-algorithm-based multiple regression with fuzzy inference system for detection of nocturnal hypoglycemic episodes

Ling, SSH

Nguyen, HT

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Information Technology in Biomedicine, 2011, 15 (2), pp. 308 - 315
Issue Date:: 2011-03-01

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Field	Value	Language
dc.contributor.author	Ling, SSH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2011-03-01	en_US
dc.identifier.citation	IEEE Transactions on Information Technology in Biomedicine, 2011, 15 (2), pp. 308 - 315	en_US
dc.identifier.issn	1089-7771	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29441
dc.description.abstract	Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures, and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemic monitor is a noninvasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients (T1DM). Based on heart rate (HR), corrected QT interval of the ECG signal, change of HR, and the change of corrected QT interval, we develop a genetic algorithm (GA)-based multiple regression with fuzzy inference system (FIS) to classify the presence of hypoglycemic episodes. GA is used to find the optimal fuzzy rules and membership functions of FIS and the model parameters of regression method. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes is associated with HRs and corrected QT intervals. The overall data were organized into a training set (eight patients) and a testing set (another eight patients) randomly selected. The results show that the proposed algorithm performs a good sensitivity with an acceptable specificity. © 2006 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Information Technology in Biomedicine	en_US
dc.relation.isbasedon	10.1109/TITB.2010.2103953	en_US
dc.subject.classification	Medical Informatics	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Hypoglycemia	en_US
dc.subject.mesh	Monitoring, Physiologic	en_US
dc.subject.mesh	Regression Analysis	en_US
dc.subject.mesh	Sleep	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Fuzzy Logic	en_US
dc.subject.mesh	Models, Genetic	en_US
dc.subject.mesh	Adolescent	en_US
dc.title	Genetic-algorithm-based multiple regression with fuzzy inference system for detection of nocturnal hypoglycemic episodes	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	15	en_US
utslib.for	090303 Biomedical Instrumentation	en_US
utslib.for	090602 Control Systems, Robotics and Automation	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	11 Medical and Health Sciences	en_US
dc.location.activity	ISI:000287896600012
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.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures, and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemic monitor is a noninvasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients (T1DM). Based on heart rate (HR), corrected QT interval of the ECG signal, change of HR, and the change of corrected QT interval, we develop a genetic algorithm (GA)-based multiple regression with fuzzy inference system (FIS) to classify the presence of hypoglycemic episodes. GA is used to find the optimal fuzzy rules and membership functions of FIS and the model parameters of regression method. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes is associated with HRs and corrected QT intervals. The overall data were organized into a training set (eight patients) and a testing set (another eight patients) randomly selected. The results show that the proposed algorithm performs a good sensitivity with an acceptable specificity. © 2006 IEEE.

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http://hdl.handle.net/10453/29441