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

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dc.contributor.author Nguyen, HT
dc.contributor.author Ghevondian, N
dc.contributor.author Jones, TW
dc.date.accessioned 2009-11-09T05:39:15Z
dc.date.issued 2006
dc.identifier.citation Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2006, pp. 6053 - 6056
dc.identifier.issn 1557-170X
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/3168
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 (1.16 +/- 0.16 vs. 1.03 +/- 0.11, P<0.0001), their corrected QT intervals increased (1.09 +/- 0.09 vs. 1.02 +/- 0.07, P<0.0001) and their skin impedances reduced significantly (0.66 +/- 0.19 vs. 0.82 +/- 0.21, 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.
dc.title Neural-network detection of hypoglycemic episodes in children with type 1 diabetes using physiological parameters.
dc.type Conference Proceeding
dc.parent Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
dc.journal.number en_US
dc.publocation New York, USA en_US
dc.publocation Piscataway, NJ, USA
dc.identifier.startpage 6053 en_US
dc.identifier.endpage 6056 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.conference IEEE Conference on Robotics, Automation and Mechatronics
dc.conference Annual International Conference of the IEEE Engineering in Medicine and Biology Society
dc.conference.location New York, USA en_US
dc.for 090305 Rehabilitation Engineering
dc.personcode 840115
dc.percentage 100 en_US
dc.classification.name 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
dc.date.activity 20060830 en_US
dc.date.activity 2007-06-07
dc.date.activity 2006-08-30
dc.location.activity New York, USA en_US
dc.location.activity Bangkok, Thailand
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Strength - Health Technologies
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true


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