The Effect of Hypoglycemia on Spectral Moments in EEG Epochs of Different Durations in Type 1 Diabetes Patients.

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

The Effect of Hypoglycemia on Spectral Moments in EEG Epochs of Different Durations in Type 1 Diabetes Patients.

Ngo, CQ Chai, R

Jones, TW Nguyen, HT

Permalink

Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Journal of Biomedical and Health Informatics, 2021, 25, (8), pp. 2857-2865
Issue Date:: 2021-08

Closed Access

	Filename	Description	Size
	The_Effect_of_Hypoglycemia_on_Spectral_Moments_in_EEG_Epochs_of_Different_Durations_in_Type_1_Diabetes_Patients.pdf		3.03 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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.accessioned	2022-05-31T03:38:07Z
dc.date.available	2022-05-31T03:38:07Z
dc.date.issued	2021-08
dc.identifier.citation	IEEE Journal of Biomedical and Health Informatics, 2021, 25, (8), pp. 2857-2865
dc.identifier.issn	2168-2208
dc.identifier.issn	2168-2208
dc.identifier.uri	http://hdl.handle.net/10453/157840
dc.description.abstract	The potential of using an electroencephalogram (EEG) to detect hypoglycemia in patients with type 1 diabetes (T1D) has been investigated in both time and frequency domains. Under hyperinsulinemic hypoglycemic clamp conditions, we have shown that the brain's response to hypoglycemic episodes could be described by the centroid frequency and spectral gyration radius evaluated from spectral moments of EEG signals. The aim of this paper is to investigate the effect of hypoglycemia on spectral moments in EEG epochs of different durations and to propose the optimal time window for hypoglycemia detection without using clamp protocols. The incidence of hypoglycemic episodes at night time in five T1D adolescents was analyzed from selected data of ten days of observations in this study. We found that hypoglycemia is associated with significant changes (P < 0.05) in spectral moments of EEG segments in different lengths. Specifically, the changes were more pronounced on the occipital lobe. We used effect size as a measure to determine the best EEG epoch duration for the detection of hypoglycemic episodes. Using Bayesian neural networks, this study showed that 30 second segments provide the best detection rate of hypoglycemia. In addition, Clarke's error grid analysis confirms the correlation between hypoglycemia and EEG spectral moments of this optimal time window, with 86% of clinically acceptable estimated blood glucose values. These results confirm the potential of using EEG spectral moments to detect the occurrence of hypoglycemia.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers
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.2021.3054876
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.subject.mesh	Adolescent
dc.subject.mesh	Algorithms
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Diabetes Mellitus, Type 1
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Humans
dc.subject.mesh	Hypoglycemia
dc.subject.mesh	Adolescent
dc.subject.mesh	Algorithms
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Diabetes Mellitus, Type 1
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Humans
dc.subject.mesh	Hypoglycemia
dc.subject.mesh	Humans
dc.subject.mesh	Diabetes Mellitus, Type 1
dc.subject.mesh	Hypoglycemia
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Algorithms
dc.subject.mesh	Adolescent
dc.title	The Effect of Hypoglycemia on Spectral Moments in EEG Epochs of Different Durations in Type 1 Diabetes Patients.
dc.type	Journal Article
utslib.citation.volume	25
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
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	2022-05-31T03:38:05Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	25
utslib.citation.issue	8

Abstract:

The potential of using an electroencephalogram (EEG) to detect hypoglycemia in patients with type 1 diabetes (T1D) has been investigated in both time and frequency domains. Under hyperinsulinemic hypoglycemic clamp conditions, we have shown that the brain's response to hypoglycemic episodes could be described by the centroid frequency and spectral gyration radius evaluated from spectral moments of EEG signals. The aim of this paper is to investigate the effect of hypoglycemia on spectral moments in EEG epochs of different durations and to propose the optimal time window for hypoglycemia detection without using clamp protocols. The incidence of hypoglycemic episodes at night time in five T1D adolescents was analyzed from selected data of ten days of observations in this study. We found that hypoglycemia is associated with significant changes (P < 0.05) in spectral moments of EEG segments in different lengths. Specifically, the changes were more pronounced on the occipital lobe. We used effect size as a measure to determine the best EEG epoch duration for the detection of hypoglycemic episodes. Using Bayesian neural networks, this study showed that 30 second segments provide the best detection rate of hypoglycemia. In addition, Clarke's error grid analysis confirms the correlation between hypoglycemia and EEG spectral moments of this optimal time window, with 86% of clinically acceptable estimated blood glucose values. These results confirm the potential of using EEG spectral moments to detect the occurrence of hypoglycemia.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/157840