| 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 |
2020-06-20T02:17:19Z |
|
|
dc.date.available |
2020-06-20T02:17:19Z |
|
|
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. 5439-5442 |
|
|
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/10453/141578
|
|
|
dc.description.abstract |
This paper presents a hypoglycemia detection system using electroencephalogram (EEG) spectral moments from 8 patients with type 1 diabetes (T1D) at night time. Four channels (C3, C4, O1, and O2) associated with glycemic episodes were analyzed. Spectral moments were applied to EEG signal and its corresponding speed and acceleration. During hypoglycemia, theta moments increased significantly (P<; 0.001) and alpha moments decreased significantly (P<; 0.001). The system used an optimal Bayesian neural network for detecting hypoglycemic episodes. Based on the optimal network architecture with the highest log evidence, the final classification results for the test set were 79% and 51% in sensitivity and specificity, respectively. Essentially, the estimated blood glucose profiles correlated significantly to actual values in the test set (P<; 0.0001). Using error grid analysis, 93% of the estimated values were clinically acceptable. |
|
|
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.2019.8857594 |
|
|
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.rights |
info:eu-repo/semantics/closedAccess |
|
|
dc.subject.mesh |
Humans |
|
|
dc.subject.mesh |
Diabetes Mellitus, Type 1 |
|
|
dc.subject.mesh |
Hypoglycemia |
|
|
dc.subject.mesh |
Blood Glucose |
|
|
dc.subject.mesh |
Hypoglycemic Agents |
|
|
dc.subject.mesh |
Electroencephalography |
|
|
dc.subject.mesh |
Bayes Theorem |
|
|
dc.subject.mesh |
Algorithms |
|
|
dc.subject.mesh |
Neural Networks, Computer |
|
|
dc.subject.mesh |
Humans |
|
|
dc.subject.mesh |
Diabetes Mellitus, Type 1 |
|
|
dc.subject.mesh |
Hypoglycemia |
|
|
dc.subject.mesh |
Blood Glucose |
|
|
dc.subject.mesh |
Hypoglycemic Agents |
|
|
dc.subject.mesh |
Electroencephalography |
|
|
dc.subject.mesh |
Bayes Theorem |
|
|
dc.subject.mesh |
Algorithms |
|
|
dc.subject.mesh |
Neural Networks, Computer |
|
|
dc.subject.mesh |
Algorithms |
|
|
dc.subject.mesh |
Bayes Theorem |
|
|
dc.subject.mesh |
Blood Glucose |
|
|
dc.subject.mesh |
Diabetes Mellitus, Type 1 |
|
|
dc.subject.mesh |
Electroencephalography |
|
|
dc.subject.mesh |
Humans |
|
|
dc.subject.mesh |
Hypoglycemia |
|
|
dc.subject.mesh |
Hypoglycemic Agents |
|
|
dc.subject.mesh |
Neural Networks, Computer |
|
|
dc.title |
Nocturnal Hypoglycemia Detection using Optimal Bayesian Algorithm in an EEG Spectral Moments Based System. |
|
|
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 |
2020-06-20T02:17:15Z |
|
|
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 |
|
