Detection of hypoglycemic episodes in children with type 1 diabetes using an optimal Bayesian neural network algorithm.

Nguyen, HT; Ghevondian, N; Nguyen, ST; Jones, TW

Detection of hypoglycemic episodes in children with type 1 diabetes using an optimal Bayesian neural network algorithm.

Nguyen, HT

Ghevondian, N Nguyen, ST Jones, TW

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Publication Type:: Conference Proceeding
Citation:: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2007, pp. 3140 - 3143
Issue Date:: 2007-12-01

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Field	Value	Language
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Ghevondian, N	en_US
dc.contributor.author	Nguyen, ST	en_US
dc.contributor.author	Jones, TW	en_US
dc.date.issued	2007-12-01	en_US
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, 2007, pp. 3140 - 3143	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2830
dc.description.abstract	Hypoglycemia or low blood glucose is a common and serious side effect of insulin therapy in patients with diabetes. 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 Bayesian neural network detection algorithm has been developed to recognize the presence of hypoglycemic episodes. From a clinical study of 25 children with T1DM, associated with hypoglycemic episodes, their heart rates increased (1.152+/-0.157 vs. 1.035+/-0.108, P < 0.0001), their corrected QT intervals increased (1.088+/-0.086 vs. 1.020+/-0.062, P < 0.0001) and their skin impedances reduced significantly (0.679+/-0.195 vs. 0.837+/-0.203, P < 0.0001). The overall data were organized into a training set (14 cases) and a test set 14 cases) randomly selected. Using an optimal Bayesian neural network with 11 hidden nodes, and an algorithm developed from the training set, a sensitivity of 0.8346 and specificity of 0.6388 were achieved for the test set.	en_US
dc.relation.ispartof	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Diabetes Mellitus, Type 1	en_US
dc.subject.mesh	Hypoglycemia	en_US
dc.subject.mesh	Diagnosis, Computer-Assisted	en_US
dc.subject.mesh	Bayes Theorem	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Artificial Intelligence	en_US
dc.subject.mesh	Child, Preschool	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	Detection of hypoglycemic episodes in children with type 1 diabetes using an optimal Bayesian neural network algorithm.	en_US
dc.type	Conference Proceeding
utslib.for	090303 Biomedical Instrumentation	en_US
dc.location.activity	Lyon, France	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/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Hypoglycemia or low blood glucose is a common and serious side effect of insulin therapy in patients with diabetes. 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 Bayesian neural network detection algorithm has been developed to recognize the presence of hypoglycemic episodes. From a clinical study of 25 children with T1DM, associated with hypoglycemic episodes, their heart rates increased (1.152+/-0.157 vs. 1.035+/-0.108, P < 0.0001), their corrected QT intervals increased (1.088+/-0.086 vs. 1.020+/-0.062, P < 0.0001) and their skin impedances reduced significantly (0.679+/-0.195 vs. 0.837+/-0.203, P < 0.0001). The overall data were organized into a training set (14 cases) and a test set 14 cases) randomly selected. Using an optimal Bayesian neural network with 11 hidden nodes, and an algorithm developed from the training set, a sensitivity of 0.8346 and specificity of 0.6388 were achieved for the test set.

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http://hdl.handle.net/10453/2830