Hybrid PSO-based variable translation wavelet neural network and its application to hypoglycemia detection system

San, PP; Ling, SH; Nguyen, HT

Hybrid PSO-based variable translation wavelet neural network and its application to hypoglycemia detection system

San, PP Ling, SH

Nguyen, HT

Permalink

Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2013, 23 (7-8), pp. 2177 - 2184
Issue Date:: 2013-12-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (305.9 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	San, PP	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Neural Computing and Applications, 2013, 23 (7-8), pp. 2177 - 2184	en_US
dc.identifier.issn	0941-0643	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26578
dc.description.abstract	To provide the detection of hypoglycemic episodes in Type 1 diabetes mellitus, hypoglycemia detection system is developed by the use of variable translation wavelet neural network (VTWNN) in this paper. A wavelet neural network with variable translation parameter is selected as a suitable classifier because of its excellent characteristics in capturing nonstationary signal analysis and nonlinear function modeling. Due to the variable translation parameters, the network becomes an adaptive network and provides better classification performance. An improved hybrid particle swarm optimization is used to train the parameters of VTWNN. Using the proposed classifier, a sensitivity of 81.40 % and a specificity of 50.91 % were achieved. The comparison results also show that the proposed detection system performs well in terms of good sensitivity and acceptable specificity. © 2012 Springer-Verlag London Limited.	en_US
dc.relation.ispartof	Neural Computing and Applications	en_US
dc.relation.isbasedon	10.1007/s00521-012-1168-y	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Hybrid PSO-based variable translation wavelet neural network and its application to hypoglycemia detection system	en_US
dc.type	Journal Article
utslib.citation.volume	7-8	en_US
utslib.citation.volume	23	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	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
dcterms.rights	The original publication is available at www.springerlink.com
pubs.issue	7-8	en_US
pubs.publication-status	Published	en_US
pubs.volume	23	en_US

Abstract:

To provide the detection of hypoglycemic episodes in Type 1 diabetes mellitus, hypoglycemia detection system is developed by the use of variable translation wavelet neural network (VTWNN) in this paper. A wavelet neural network with variable translation parameter is selected as a suitable classifier because of its excellent characteristics in capturing nonstationary signal analysis and nonlinear function modeling. Due to the variable translation parameters, the network becomes an adaptive network and provides better classification performance. An improved hybrid particle swarm optimization is used to train the parameters of VTWNN. Using the proposed classifier, a sensitivity of 81.40 % and a specificity of 50.91 % were achieved. The comparison results also show that the proposed detection system performs well in terms of good sensitivity and acceptable specificity. © 2012 Springer-Verlag London Limited.

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

http://hdl.handle.net/10453/26578