Nocturnal Hypoglycemia Detection using EEG Spectral Moments under Natural Occurrence Conditions.

Ngo, CQ; Chai, R; Nguyen, TV; Jones, TW; Nguyen, HT

Nocturnal Hypoglycemia Detection using EEG Spectral Moments under Natural Occurrence Conditions.

Ngo, CQ Chai, R

Nguyen, TV Jones, TW Nguyen, HT

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Publisher:: IEEE
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. Annual Conference, 2019, 2019, pp. 7177-7180
Issue Date:: 2019-07

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Field	Value	Language
dc.contributor.author	Ngo, CQ
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024
dc.contributor.author	Nguyen, TV
dc.contributor.author	Jones, TW
dc.contributor.author	Nguyen, HT
dc.date	2019-07-23
dc.date.accessioned	2020-06-20T02:23:58Z
dc.date.available	2020-06-20T02:23:58Z
dc.date.issued	2019-07
dc.identifier.citation	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2019, 2019, pp. 7177-7180
dc.identifier.isbn	978-1-5386-1311-5
dc.identifier.issn	1557-170X
dc.identifier.issn	1558-4615
dc.identifier.uri	http://hdl.handle.net/10453/141579
dc.description.abstract	This paper is concerned with a study of hypoglycemia under natural occurrence conditions at night time. Five adolescents with type 1 diabetes (T1D) participated in the experiments. Patients' blood glucose profiles were interpolated to estimate the intermediate values. The proposed system used spectral moments of electroencephalogram (EEG) signals from central and occipital areas as features for detecting hypoglycemia. We found that hypoglycemia could be detected non-invasively using EEG spectral moments. During hypoglycemic episodes, theta moments increased significantly (P<; 0.005) whereas beta moments decreased significantly (P<; 0.001). Based on the optimal network architecture associated with the highest log evidence, the proposed optimal Bayesian neural network resulted in a sensitivity of 82% and a specificity of 52%. In addition, the estimated blood glucose profiles showed a significant correlation (P<; 1e-6) with interpolated blood glucose values in the test set.
dc.format	Print
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/nhmrc/1102286
dc.relation.ispartof	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
dc.relation.ispartof	Annual International Conference of the IEEE Engineering in Medicine and Biology Society
dc.relation.isbasedon	10.1109/embc.2019.8856695
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Nocturnal Hypoglycemia Detection using EEG Spectral Moments under Natural Occurrence Conditions.
dc.type	Conference Proceeding
utslib.citation.volume	2019
utslib.location.activity	Berlin, Germany
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-20T02:23:55Z
pubs.finish-date	2019-07-27
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-07-23
pubs.volume	2019
dc.location	Piscataway, USA

Abstract:

This paper is concerned with a study of hypoglycemia under natural occurrence conditions at night time. Five adolescents with type 1 diabetes (T1D) participated in the experiments. Patients' blood glucose profiles were interpolated to estimate the intermediate values. The proposed system used spectral moments of electroencephalogram (EEG) signals from central and occipital areas as features for detecting hypoglycemia. We found that hypoglycemia could be detected non-invasively using EEG spectral moments. During hypoglycemic episodes, theta moments increased significantly (P<; 0.005) whereas beta moments decreased significantly (P<; 0.001). Based on the optimal network architecture associated with the highest log evidence, the proposed optimal Bayesian neural network resulted in a sensitivity of 82% and a specificity of 52%. In addition, the estimated blood glucose profiles showed a significant correlation (P<; 1e-6) with interpolated blood glucose values in the test set.

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http://hdl.handle.net/10453/141579