Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia.

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

Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia.

Ngo, CQ Chai, R

Nguyen, TV Jones, TW Nguyen, HT

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE journal of biomedical and health informatics, 2020, 24, (5), pp. 1237-1245
Issue Date:: 2020-05

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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.accessioned	2020-12-27T19:09:09Z
dc.date.available	2020-12-27T19:09:09Z
dc.date.issued	2020-05
dc.identifier.citation	IEEE journal of biomedical and health informatics, 2020, 24, (5), pp. 1237-1245
dc.identifier.issn	2168-2194
dc.identifier.issn	2168-2208
dc.identifier.uri	http://hdl.handle.net/10453/144961
dc.description.abstract	Hypoglycemia or low blood glucose is the most feared complication of insulin treatment of diabetes. For people with diabetes, the mismatch between the insulin therapy and the body's physiology could increase the risk of hypoglycemia. Nocturnal hypoglycemia is particularly dangerous for type-1 diabetes patients because its symptoms may obscure during sleep. The early onset detection of hypoglycemia at night time is necessary because it can result in unconsciousness and even death. This paper presents new electroencephalogram spectral features for nocturnal hypoglycemia detection. The system uses high-order spectral moments for feature extraction and Bayesian neural network for classification. From a clinical study of hypoglycemia of eight patients with type-1 diabetes at night, we find that these spectral moments of theta band and alpha band changed significantly. During hypoglycemia episodes, the theta moments increased significantly (P < 0.001) while the features of alpha band reduced significantly (P < 0.001). Using the optimal Bayesian neural network, the classification results were 85% and 52% in sensitivity and specificity, respectively. The significant correlation (P < 0.001) with real blood glucose profiles shows the effectiveness of the proposed features for the detection of nocturnal hypoglycemia.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/nhmrc/1102286
dc.relation.ispartof	IEEE journal of biomedical and health informatics
dc.relation.isbasedon	10.1109/jbhi.2019.2931782
dc.rights	© 2020 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.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 11 Medical and Health Sciences
dc.subject.classification	Medical Informatics
dc.title	Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia.
dc.type	Journal Article
utslib.citation.volume	24
utslib.location.activity	United States
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	11 Medical and Health Sciences
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	*
dc.date.updated	2020-12-27T19:08:51Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	24
utslib.citation.issue	5

Abstract:

Hypoglycemia or low blood glucose is the most feared complication of insulin treatment of diabetes. For people with diabetes, the mismatch between the insulin therapy and the body's physiology could increase the risk of hypoglycemia. Nocturnal hypoglycemia is particularly dangerous for type-1 diabetes patients because its symptoms may obscure during sleep. The early onset detection of hypoglycemia at night time is necessary because it can result in unconsciousness and even death. This paper presents new electroencephalogram spectral features for nocturnal hypoglycemia detection. The system uses high-order spectral moments for feature extraction and Bayesian neural network for classification. From a clinical study of hypoglycemia of eight patients with type-1 diabetes at night, we find that these spectral moments of theta band and alpha band changed significantly. During hypoglycemia episodes, the theta moments increased significantly (P < 0.001) while the features of alpha band reduced significantly (P < 0.001). Using the optimal Bayesian neural network, the classification results were 85% and 52% in sensitivity and specificity, respectively. The significant correlation (P < 0.001) with real blood glucose profiles shows the effectiveness of the proposed features for the detection of nocturnal hypoglycemia.

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http://hdl.handle.net/10453/144961