Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes
- Publication Type:
- Journal Article
- Citation:
- ISA Transactions, 2016, 64 pp. 440 - 446
- Issue Date:
- 2016-09-01
Open Access
Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author |
Ling, SH |
en_US |
dc.contributor.author | San, PP | en_US |
dc.contributor.author |
Nguyen, HT |
en_US |
dc.date.available | 2016-05-12 | en_US |
dc.date.issued | 2016-09-01 | en_US |
dc.identifier.citation | ISA Transactions, 2016, 64 pp. 440 - 446 | en_US |
dc.identifier.issn | 0019-0578 | en_US |
dc.identifier.uri | http://hdl.handle.net/10453/44117 | |
dc.description.abstract | © 2016 ISA Hypoglycemia is a very common in type 1 diabetic persons and can occur at any age. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances. Because of that, an accurate continuing hypoglycemia monitoring system is a very important medical device for diabetic patients. In this paper, we proposed a non-invasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal. To enhance the detection accuracy, extreme learning machine (ELM) is developed to recognize the presence of hypoglycemia. A clinical study of 16 children with T1DM is given to illustrate the good performance of ELM. | en_US |
dc.relation | http://purl.org/au-research/grants/nhmrc/1102286 | |
dc.relation | http://purl.org/au-research/grants/nhmrc/APP1102286 | |
dc.relation.ispartof | ISA Transactions | en_US |
dc.relation.isbasedon | 10.1016/j.isatra.2016.05.008 | en_US |
dc.subject.classification | Industrial Engineering & Automation | en_US |
dc.subject.mesh | Humans | en_US |
dc.subject.mesh | Long QT Syndrome | en_US |
dc.subject.mesh | Diabetes Mellitus, Type 1 | en_US |
dc.subject.mesh | Hypoglycemia | en_US |
dc.subject.mesh | Blood Glucose | en_US |
dc.subject.mesh | Electrocardiography | en_US |
dc.subject.mesh | Reproducibility of Results | en_US |
dc.subject.mesh | Heart Rate | en_US |
dc.subject.mesh | Algorithms | en_US |
dc.subject.mesh | Neural Networks (Computer) | en_US |
dc.subject.mesh | Computer Simulation | en_US |
dc.subject.mesh | Adolescent | en_US |
dc.subject.mesh | Child | en_US |
dc.subject.mesh | Female | en_US |
dc.subject.mesh | Male | en_US |
dc.subject.mesh | Machine Learning | en_US |
dc.subject.mesh | Adolescent | en_US |
dc.subject.mesh | Algorithms | en_US |
dc.subject.mesh | Blood Glucose | en_US |
dc.subject.mesh | Child | en_US |
dc.subject.mesh | Computer Simulation | en_US |
dc.subject.mesh | Diabetes Mellitus, Type 1 | en_US |
dc.subject.mesh | Electrocardiography | en_US |
dc.subject.mesh | Female | en_US |
dc.subject.mesh | Heart Rate | en_US |
dc.subject.mesh | Humans | en_US |
dc.subject.mesh | Hypoglycemia | en_US |
dc.subject.mesh | Long QT Syndrome | en_US |
dc.subject.mesh | Machine Learning | en_US |
dc.subject.mesh | Male | en_US |
dc.subject.mesh | Neural Networks (Computer) | en_US |
dc.subject.mesh | Reproducibility of Results | en_US |
dc.title | Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes | en_US |
dc.type | Journal Article | |
utslib.description.version | Published | en_US |
utslib.citation.volume | 64 | en_US |
utslib.for | 0906 Electrical And Electronic Engineering | en_US |
utslib.for | 0910 Manufacturing Engineering | en_US |
utslib.for | 0906 Electrical And Electronic Engineering | en_US |
pubs.embargo.period | Not known | en_US |
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/Strength - CHT - Health Technologies | |
utslib.copyright.status | open_access | |
utslib.copyright.embargo | 2018-06-13T00:00:00+1000 | |
pubs.publication-status | Published | en_US |
pubs.volume | 64 | en_US |
Files in This Item:
Filename | Description | Size | |||
---|---|---|---|---|---|
ISATrans-Ling 2016_.pdf | Accepted Manuscript Version | 608.72 kB | Adobe PDF |
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is open access.
The embargo period expires on 13 Jun 2018
Abstract:
© 2016 ISA Hypoglycemia is a very common in type 1 diabetic persons and can occur at any age. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances. Because of that, an accurate continuing hypoglycemia monitoring system is a very important medical device for diabetic patients. In this paper, we proposed a non-invasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal. To enhance the detection accuracy, extreme learning machine (ELM) is developed to recognize the presence of hypoglycemia. A clinical study of 16 children with T1DM is given to illustrate the good performance of ELM.
Please use this identifier to cite or link to this item:
Not enough data to produce graph