A CHF detection method based on deep learning with RR intervals

Chen, W; Liu, G; Su, S; Jiang, Q; Hung, N

A CHF detection method based on deep learning with RR intervals

Chen, W Liu, G Su, S

Jiang, Q Hung, N

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017, pp. 3369 - 3372
Issue Date:: 2017-09-13

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Field	Value	Language
dc.contributor.author	Chen, W	en_US
dc.contributor.author	Liu, G	en_US
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852	en_US
dc.contributor.author	Jiang, Q	en_US
dc.contributor.author	Hung, N https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2017-09-13	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017, pp. 3369 - 3372	en_US
dc.identifier.isbn	9781509028092	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115470
dc.description.abstract	© 2017 IEEE. There are extensive studies investigating congestive heart failure (CHF) detection based on heart rate variability. Although a high level of accuracy has been achieved, its robustness under different conditions is not guaranteed. To improve the robustness, we applied sparse auto-encoder-based deep learning algorithm in CHF detection with RR intervals. A total data size of 30,592 (5-min RR interval) was obtained from 72 healthy persons and 44 CHF patients. The deep learning algorithm first extracts unsupervised features using a sparse auto-encoder from raw RR intervals, then constructs a deep neural network model with various hidden nodes combinations. Results showed that the model achieved 72.41% accuracy. This demonstrated that RR intervals have potential in CHF detection but cannot fully reflect dynamic change in 24-h.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2017.8037578	en_US
dc.subject.mesh	Humans	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	Heart Failure	en_US
dc.subject.mesh	Machine Learning	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	A CHF detection method based on deep learning with RR intervals	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0903 Biomedical 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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. There are extensive studies investigating congestive heart failure (CHF) detection based on heart rate variability. Although a high level of accuracy has been achieved, its robustness under different conditions is not guaranteed. To improve the robustness, we applied sparse auto-encoder-based deep learning algorithm in CHF detection with RR intervals. A total data size of 30,592 (5-min RR interval) was obtained from 72 healthy persons and 44 CHF patients. The deep learning algorithm first extracts unsupervised features using a sparse auto-encoder from raw RR intervals, then constructs a deep neural network model with various hidden nodes combinations. Results showed that the model achieved 72.41% accuracy. This demonstrated that RR intervals have potential in CHF detection but cannot fully reflect dynamic change in 24-h.

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http://hdl.handle.net/10453/115470