Neural-network detection of hypoglycemic episodes in children with type 1 diabetes using physiological responses

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Show simple item record Nguyen, Hung en_US Ghevondian, Nejhdeh en_US Jones, Timothy en_US
dc.contributor.editor N/A en_US 2009-11-09T05:39:15Z 2009-11-09T05:39:15Z 2006 en_US
dc.identifier 2006005641 en_US
dc.identifier.citation Nguyen Hung, Ghevondian Nejhdeh, and Jones Timothy 2006, 'Neural-network detection of hypoglycemic episodes in children with type 1 diabetes using physiological responses', IEEE, New York, USA, pp. 6053-6056. en_US
dc.identifier.issn 14244-0033-3 en_US
dc.identifier.other E1 en_US
dc.description.abstract The most common and highly feared adverse effect of intensive insulin therapy in patients with diabetes is the increased risk of hypoglycemia. Symptoms of hypoglycemia arise from the activation of the autonomous central nervous systems and from reduced cerebral glucose consumption. HypoMon is a non-invasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in Type 1 diabetes mellitus patients (T1DM). Based on heart rate, corrected QT interval of the ECG signal and skin impedance, a neural network detection algorithm has been developed to recognize the presence of hypoglycemic episodes. From a clinical study of 21 children with T1DM, associated with hypoglycemic episodes, their heart rates increased (1.16plusmn0.16 vs. 1.03plusmn0.11, P<0.0001), their corrected QT intervals increased (1.09plusmn0.09 vs. 1.02plusmn0.07, P<0.0001) and their skin impedances reduced significantly (0.66plusmn0.19 vs. 0.82plusmn0.21, P<0.0001). The overall data were obtained and grouped into a training set, a validation set and a test set, each with 7 patients randomly selected. Using a feedforward multi-layer neural network with 9 hidden nodes, and an algorithm developed from the training set and the validation set, a sensitivity of 0.9516 and specificity of 0.4142 were achieved for the test set. A more advanced neural network algorithm will be developed to improve the specificity of test sets in the near future en_US
dc.publisher IEEE en_US
dc.relation.isbasedon en_US
dc.title Neural-network detection of hypoglycemic episodes in children with type 1 diabetes using physiological responses en_US
dc.parent Proceedings of the 28th IEEE EMBS Annual International Conference en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation New York, USA en_US
dc.identifier.startpage 6053 en_US
dc.identifier.endpage 6056 en_US FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.conference.location New York, USA en_US
dc.for 090305 en_US
dc.personcode 840115 en_US
dc.personcode 0000020765 en_US
dc.personcode 0000023264 en_US
dc.percentage 100 en_US Rehabilitation Engineering en_US
dc.classification.type FOR-08 en_US
dc.custom Annual International Conference of the IEEE Engineering in Medicine and Biology Society en_US 20060830 en_US
dc.location.activity New York, USA en_US
dc.description.keywords Type 1 diabetes, hypoglycemia, neural network en_US

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