Real-time detection of nocturnal hypoglycemic episodes using a novel non-invasive hypoglycemia monitor.

Nguyen, HT; Ghevondian, N; Jones, TW

Real-time detection of nocturnal hypoglycemic episodes using a novel non-invasive hypoglycemia monitor.

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, 2009, pp. 3822 - 3825
Issue Date:: 2009-12-01

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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	2009-12-01	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, 2009, pp. 3822 - 3825	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12008
dc.description.abstract	Hypoglycemia or low blood glucose is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemia is unpleasant and can result in unconsciousness, seizures and even death. HypoMon is a realtime non-invasive monitor that measures relevant physiological parameters continuously to provide detection of hypoglycemic episodes in Type 1 diabetes mellitus patients (T1DM). Based on heart rate and corrected QT interval of the ECG signal, we have continued to develop effective algorithms for early detection of nocturnal hypoglycemia. From a clinical study of 24 children with T1DM, associated with natural occurrence of hypoglycemic episodes at night, their heart rates increased (1.021+/-0.264 vs. 1.068+/-0.314, P < 0.053) and their corrected QT intervals increased significantly (1.030+/-0.079 vs. 1.052+/-0.078, P < 0.002). It is interesting to note that QT interval and heart rate are less correlated when the patients experienced hypoglycemic episodes through natural occurrence compared to when clamp studies were performed. The overall data were organized into a training set (12 patients) and a test set (another 12 patients) randomly selected. Using the optimal Bayesian neural network which was derived from the training set with the highest log evidence, the estimated blood glucose profiles produced a significant correlation (P < 0.02) against measured values in 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	Glucose	en_US
dc.subject.mesh	Blood Glucose	en_US
dc.subject.mesh	Monitoring, Ambulatory	en_US
dc.subject.mesh	Models, Statistical	en_US
dc.subject.mesh	Bayes Theorem	en_US
dc.subject.mesh	Heart Rate	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Computer Systems	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	Child	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	Real-time detection of nocturnal hypoglycemic episodes using a novel non-invasive hypoglycemia monitor.	en_US
dc.type	Conference Proceeding
utslib.for	090303 Biomedical Instrumentation	en_US
dc.location.activity	Minneapolis, Minnesota, USA	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. Hypoglycemia is unpleasant and can result in unconsciousness, seizures and even death. HypoMon is a realtime non-invasive monitor that measures relevant physiological parameters continuously to provide detection of hypoglycemic episodes in Type 1 diabetes mellitus patients (T1DM). Based on heart rate and corrected QT interval of the ECG signal, we have continued to develop effective algorithms for early detection of nocturnal hypoglycemia. From a clinical study of 24 children with T1DM, associated with natural occurrence of hypoglycemic episodes at night, their heart rates increased (1.021+/-0.264 vs. 1.068+/-0.314, P < 0.053) and their corrected QT intervals increased significantly (1.030+/-0.079 vs. 1.052+/-0.078, P < 0.002). It is interesting to note that QT interval and heart rate are less correlated when the patients experienced hypoglycemic episodes through natural occurrence compared to when clamp studies were performed. The overall data were organized into a training set (12 patients) and a test set (another 12 patients) randomly selected. Using the optimal Bayesian neural network which was derived from the training set with the highest log evidence, the estimated blood glucose profiles produced a significant correlation (P < 0.02) against measured values in the test set.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/12008