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

Nguyen, HT; Ghevondian, N; Jones, TW

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

Nguyen, HT

Ghevondian, N 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, 2006, pp. 6053 - 6056
Issue Date:: 2006-12-01

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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	Jones, TW	en_US
dc.date.issued	2006-12-01	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, 2006, pp. 6053 - 6056	en_US
dc.identifier.issn	1557-170X	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.	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	Central Nervous System	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	Glucose	en_US
dc.subject.mesh	Blood Glucose	en_US
dc.subject.mesh	Monitoring, Physiologic	en_US
dc.subject.mesh	Glucose Clamp Technique	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Heart Rate	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Adolescent	en_US
dc.subject.mesh	Child	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.subject.mesh	Software	en_US
dc.title	Neural-network detection of hypoglycemic episodes in children with type 1 diabetes using physiological parameters.	en_US
dc.type	Conference Proceeding
utslib.for	090305 Rehabilitation Engineering	en_US
dc.location.activity	New York, USA	en_US
dc.location.activity	Bangkok, Thailand
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:

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.

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http://hdl.handle.net/10453/3168