Industrial application of evolvable block-based neural network to hypoglycemia monitoring system

San, PP; Ling, SH; Nguyen, HT

Industrial application of evolvable block-based neural network to hypoglycemia monitoring system

San, PP Ling, SH

Nguyen, HT

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Electronics, 2013, 60 (12), pp. 5892 - 5901
Issue Date:: 2013-01-01

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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	2013-01-01	en_US
dc.identifier.citation	IEEE Transactions on Industrial Electronics, 2013, 60 (12), pp. 5892 - 5901	en_US
dc.identifier.issn	0278-0046	en_US
dc.identifier.uri	http://hdl.handle.net/10453/23620
dc.description.abstract	Insulin-dependent diabetes mellitus is classified as type 1 diabetes mellitus (T1DM), and it can be further classified as immune-mediated or idiopathic. It is dangerous and can result in unconsciousness, seizures, and even sudden death. The most common physiological parameters to be effected from a hypoglycemic reaction are heart rate and corrected $QT$ interval of the electrocardiogram (ECG) signal. Considering the correlation between physiological parameters of an ECG signal and the status of hypoglycemia, a noninvasive hypoglycemia monitoring system is tested and introduced by proposing a hybrid particle-swarm-optimization-based block-based neural network (BBNN) algorithm. The proposed BBNN model offers advantages over conventional neural networks by performing the simultaneous optimization of both structure and weights. The hybrid particle swarm optimization with wavelet mutation searches for optimized structure and network parameters through particle information over a search space. All the actual data sets of 15 T1DM children were collected at the Department of Health, Government of Western Australia. Several experiments showed that the proposed BBNN performed well in terms of better sensitivity and specificity. © 1982-2012 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Industrial Electronics	en_US
dc.relation.isbasedon	10.1109/TIE.2012.2228143	en_US
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	Industrial application of evolvable block-based neural network to hypoglycemia monitoring system	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	60	en_US
utslib.for	090303 Biomedical Instrumentation	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 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	closed_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	60	en_US

Abstract:

Insulin-dependent diabetes mellitus is classified as type 1 diabetes mellitus (T1DM), and it can be further classified as immune-mediated or idiopathic. It is dangerous and can result in unconsciousness, seizures, and even sudden death. The most common physiological parameters to be effected from a hypoglycemic reaction are heart rate and corrected $QT$ interval of the electrocardiogram (ECG) signal. Considering the correlation between physiological parameters of an ECG signal and the status of hypoglycemia, a noninvasive hypoglycemia monitoring system is tested and introduced by proposing a hybrid particle-swarm-optimization-based block-based neural network (BBNN) algorithm. The proposed BBNN model offers advantages over conventional neural networks by performing the simultaneous optimization of both structure and weights. The hybrid particle swarm optimization with wavelet mutation searches for optimized structure and network parameters through particle information over a search space. All the actual data sets of 15 T1DM children were collected at the Department of Health, Government of Western Australia. Several experiments showed that the proposed BBNN performed well in terms of better sensitivity and specificity. © 1982-2012 IEEE.

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http://hdl.handle.net/10453/23620