Detection of nocturnal hypoglycemic episodes using EEG signals

Nguyen, HT; Jones, TW

Detection of nocturnal hypoglycemic episodes using EEG signals

Nguyen, HT

Jones, TW

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Publication Type:: Conference Proceeding
Citation:: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, 2010, pp. 4930 - 4933
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Jones, TW	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, 2010, pp. 4930 - 4933	en_US
dc.identifier.isbn	9781424441235	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16350
dc.description.abstract	Hypoglycemia (low blood glucose) or the fear of hypoglycemia constitutes a significant barrier to the achievement of good glycemic control in the insulin treated diabetic patients. By measuring physiological responses derived from EEG and analyzing these, we establish that hypoglycemia can be detected non-invasively. From a clinical study of six children with type 1 diabetes (T1D), associated with hypoglycemic episodes at night, their centroid (centre of gravity) alpha frequency reduced significantly (P<0.001) and their centroid theta frequency increased significantly (P<0.02). The overall data were organized into a training set (3 patients) and a test set (another 3 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<0.005) against measured values in the test set. © 2010 IEEE.	en_US
dc.relation.ispartof	2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10	en_US
dc.relation.isbasedon	10.1109/IEMBS.2010.5627233	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Hypoglycemia	en_US
dc.subject.mesh	Diagnosis, Computer-Assisted	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Monitoring, Ambulatory	en_US
dc.subject.mesh	Polysomnography	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.title	Detection of nocturnal hypoglycemic episodes using EEG signals	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
dc.location.activity	Buenos Aires, Argentina	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 (low blood glucose) or the fear of hypoglycemia constitutes a significant barrier to the achievement of good glycemic control in the insulin treated diabetic patients. By measuring physiological responses derived from EEG and analyzing these, we establish that hypoglycemia can be detected non-invasively. From a clinical study of six children with type 1 diabetes (T1D), associated with hypoglycemic episodes at night, their centroid (centre of gravity) alpha frequency reduced significantly (P<0.001) and their centroid theta frequency increased significantly (P<0.02). The overall data were organized into a training set (3 patients) and a test set (another 3 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<0.005) against measured values in the test set. © 2010 IEEE.

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http://hdl.handle.net/10453/16350