Atrial fibrillation detection using support vector machine and electrocardiographic descriptive statistics

Nuryani, N; Harjito, B; Yahya, I; Solikhah, M; Chai, R; Lestari, A

Atrial fibrillation detection using support vector machine and electrocardiographic descriptive statistics

Nuryani, N Harjito, B Yahya, I Solikhah, M Chai, R

Lestari, A

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Publication Type:: Journal Article
Citation:: International Journal of Biomedical Engineering and Technology, 2017, 24 (3), pp. 225 - 236
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Nuryani, N	en_US
dc.contributor.author	Harjito, B	en_US
dc.contributor.author	Yahya, I	en_US
dc.contributor.author	Solikhah, M	en_US
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024	en_US
dc.contributor.author	Lestari, A	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	International Journal of Biomedical Engineering and Technology, 2017, 24 (3), pp. 225 - 236	en_US
dc.identifier.issn	1752-6418	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123612
dc.description.abstract	Copyright © 2017 Inderscience Enterprises Ltd. This paper proposes a new technique for detecting atrial fibrillation (AF). The method employs electrocardiographic features and support vector machine (SVM). The features include descriptive statistics of electrocardiographic RR interval. The RR interval is the distance in time between two consecutive R-peaks of electrocardiogram. AF detections using SVM with different electrocardiographic features and different SVM free parameters are explored. Employing SVM with the optimal free parameters and all the proposed electrocardiographic features, we find an AF detection technique with a comparable performance. The best performance obtained by the technique is 98.47% and 97.84%, in terms of sensitivity and specificity.	en_US
dc.relation.ispartof	International Journal of Biomedical Engineering and Technology	en_US
dc.relation.isbasedon	10.1504/IJBET.2017.085140	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Atrial fibrillation detection using support vector machine and electrocardiographic descriptive statistics	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	24	en_US
utslib.for	0802 Computation Theory and Mathematics	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
utslib.copyright.status	open_access	*
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	24	en_US

Abstract:

Copyright © 2017 Inderscience Enterprises Ltd. This paper proposes a new technique for detecting atrial fibrillation (AF). The method employs electrocardiographic features and support vector machine (SVM). The features include descriptive statistics of electrocardiographic RR interval. The RR interval is the distance in time between two consecutive R-peaks of electrocardiogram. AF detections using SVM with different electrocardiographic features and different SVM free parameters are explored. Employing SVM with the optimal free parameters and all the proposed electrocardiographic features, we find an AF detection technique with a comparable performance. The best performance obtained by the technique is 98.47% and 97.84%, in terms of sensitivity and specificity.

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http://hdl.handle.net/10453/123612