Electroencephalogram Reactivity to Hyperglycemia in Patients with Type 1 Diabetes.

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

Electroencephalogram Reactivity to Hyperglycemia in Patients with Type 1 Diabetes.

Ngo, CQ Chai, R

Jones, TW Nguyen, HT

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020, 2020, pp. 5224-5227
Issue Date:: 2020-07

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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	Jones, TW
dc.contributor.author	Nguyen, HT
dc.date	2020-07-20
dc.date.accessioned	2021-06-10T05:17:39Z
dc.date.available	2021-06-10T05:17:39Z
dc.date.issued	2020-07
dc.identifier.citation	2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020, 2020, pp. 5224-5227
dc.identifier.isbn	978-1-7281-1990-8
dc.identifier.issn	2375-7477
dc.identifier.issn	2694-0604
dc.identifier.uri	http://hdl.handle.net/10453/149485
dc.description.abstract	This paper is concerned with a study of hyperglycemia on four patients with type 1 diabetes at night time. We investigated the association between hyperglycemic episodes and electroencephalogram (EEG) signals using data from the central and occipital areas. The power spectral density of the brain waves was estimated to compare the difference between hyperglycemia and euglycemia using the hyperglycemic threshold of 8.3 mmol/L. The statistical results showed that alpha and beta bands were more sensitive to hyperglycemic episodes than delta and theta bands. During hyperglycemia, whereas the alpha power increased significantly in the occipital lobe (P<0.005), the power of the beta band increased significantly in all observed channels (P<0.01). Using the Pearson correlation, we assessed the relationship between EEG signals and glycemic episodes. The estimated EEG power levels of the alpha band and the beta band produced a significant correlation against blood glucose levels (P<0.005). These preliminary results show the potential of using EEG signals as a biomarker to detect hyperglycemia.
dc.format	Print
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/nhmrc/1102286
dc.relation.ispartof	2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
dc.relation.ispartof	International Conference of the IEEE Engineering in Medicine & Biology Society
dc.relation.isbasedon	10.1109/embc44109.2020.9175485
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.mesh	Blood Glucose
dc.subject.mesh	Brain Waves
dc.subject.mesh	Diabetes Mellitus, Type 1
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Humans
dc.subject.mesh	Hyperglycemia
dc.subject.mesh	Humans
dc.subject.mesh	Diabetes Mellitus, Type 1
dc.subject.mesh	Hyperglycemia
dc.subject.mesh	Blood Glucose
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Brain Waves
dc.subject.mesh	Blood Glucose
dc.subject.mesh	Brain Waves
dc.subject.mesh	Diabetes Mellitus, Type 1
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Humans
dc.subject.mesh	Hyperglycemia
dc.title	Electroencephalogram Reactivity to Hyperglycemia in Patients with Type 1 Diabetes.
dc.type	Conference Proceeding
utslib.citation.volume	2020
utslib.location.activity	Canada
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/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-06-10T05:17:38Z
pubs.finish-date	2020-07-24
pubs.publication-status	Published
pubs.start-date	2020-07-20
pubs.volume	2020

Abstract:

This paper is concerned with a study of hyperglycemia on four patients with type 1 diabetes at night time. We investigated the association between hyperglycemic episodes and electroencephalogram (EEG) signals using data from the central and occipital areas. The power spectral density of the brain waves was estimated to compare the difference between hyperglycemia and euglycemia using the hyperglycemic threshold of 8.3 mmol/L. The statistical results showed that alpha and beta bands were more sensitive to hyperglycemic episodes than delta and theta bands. During hyperglycemia, whereas the alpha power increased significantly in the occipital lobe (P<0.005), the power of the beta band increased significantly in all observed channels (P<0.01). Using the Pearson correlation, we assessed the relationship between EEG signals and glycemic episodes. The estimated EEG power levels of the alpha band and the beta band produced a significant correlation against blood glucose levels (P<0.005). These preliminary results show the potential of using EEG signals as a biomarker to detect hyperglycemia.

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http://hdl.handle.net/10453/149485