Occipital EEG Activity for the Detection of Nocturnal Hypoglycemia

Ngo, CQ; Truong, BCQ; Jones, TW; Nguyen, HT

Occipital EEG Activity for the Detection of Nocturnal Hypoglycemia

Ngo, CQ Truong, BCQ Jones, TW Nguyen, HT

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018, 2018-July pp. 3862 - 3865
Issue Date:: 2018-10-26

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Field	Value	Language
dc.contributor.author	Ngo, CQ	en_US
dc.contributor.author	Truong, BCQ	en_US
dc.contributor.author	Jones, TW	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2018-10-26	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018, 2018-July pp. 3862 - 3865	en_US
dc.identifier.isbn	9781538636466	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133975
dc.description.abstract	© 2018 IEEE. Nocturnal hypoglycemia is dangerous that threatens patients because of its unclear symptoms during sleep. This paper is a study of hypoglycemia from 8 patients with type 1 diabetes (T1D) at night. O1 and O2 EEG data of the occipital lobe associated with glycemic episodes were analyzed. Frequency features were computed from Power Spectral Density using Welch's method. Centroid alpha frequency reduced significantly (P < 0.0001) while centroid theta increased considerably (P < 0.01). Spectral entropy of the unified theta-alpha band rose significantly (P < 0.005). These occipital features acted as the input of a Bayesian regularized neural network for detecting hypoglycemic episodes. The classification results were 73% and 60% of sensitivity and specificity, respectively.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2018.8513069	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	Electroencephalography	en_US
dc.subject.mesh	Bayes Theorem	en_US
dc.subject.mesh	Algorithms	en_US
dc.title	Occipital EEG Activity for the Detection of Nocturnal Hypoglycemia	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-July	en_US
utslib.for	1116 Medical Physiology	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
pubs.volume	2018-July	en_US

Abstract:

© 2018 IEEE. Nocturnal hypoglycemia is dangerous that threatens patients because of its unclear symptoms during sleep. This paper is a study of hypoglycemia from 8 patients with type 1 diabetes (T1D) at night. O1 and O2 EEG data of the occipital lobe associated with glycemic episodes were analyzed. Frequency features were computed from Power Spectral Density using Welch's method. Centroid alpha frequency reduced significantly (P < 0.0001) while centroid theta increased considerably (P < 0.01). Spectral entropy of the unified theta-alpha band rose significantly (P < 0.005). These occipital features acted as the input of a Bayesian regularized neural network for detecting hypoglycemic episodes. The classification results were 73% and 60% of sensitivity and specificity, respectively.

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http://hdl.handle.net/10453/133975