Genetic algorithm based fuzzy multiple regression for the nocturnal hypoglycaemia detection

DSpace/Manakin Repository

Search OPUS


Advanced Search

Browse

My Account

Show simple item record

dc.contributor.author Ling, SH
dc.contributor.author Nguyen, H
dc.contributor.author Chan, KY
dc.date.accessioned 2012-02-02T11:07:38Z
dc.date.issued 2010
dc.identifier.citation 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, 2010
dc.identifier.isbn 9781424469109
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/16206
dc.description.abstract Low blood glucose (Hypoglycaemia) is dangerous and can result in unconsciousness, seizures and even death. It has a common and serious side effect of insulin therapy in patients with diabetes. We measure physiological parameters (heart rate, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval) continuously to provide detection of hypoglycaemic. Based on these physiological parameters, we have developed a genetic algorithm based multiple regression model to determine the presence of hypoglycaemic episodes. Genetic algorithm is used to determine the optimal parameters of the multiple regression. The overall data were organized into a training set (8 patients) and a testing set (another 8 patient) which are randomly selected. The clinical results show that the proposed algorithm can achieve predictions with good sensitivities and acceptable specificities. © 2010 IEEE.
dc.relation.hasversion Accepted manuscript version
dc.relation.isbasedon 10.1109/CEC.2010.5586315
dc.title Genetic algorithm based fuzzy multiple regression for the nocturnal hypoglycaemia detection
dc.type Conference Proceeding
dc.description.version Published
dc.parent 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 2659 en_US
dc.identifier.endpage 2664 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 840115
dc.personcode 106694
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom IEEE Congress on Evolutionary Computation en_US
dc.date.activity 20100718 en_US
dc.location.activity Barcelona, Spain en_US
pubs.embargo.period Not known
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 Elec, Mech and Mechatronic Systems
pubs.organisational-group /University of Technology Sydney/Strength - Health Technologies
utslib.copyright.status Open Access
utslib.copyright.date 2015-04-15 12:23:47.074767+10
pubs.consider-herdc true
utslib.collection.history General (ID: 2)


Files in this item

This item appears in the following Collection(s)

Show simple item record