Ventricular tachycardia/fibrillation detection algorithm for 24/7 personal wireless heart monitoring

Fokkenrood, S; Leijdekkers, P; Gay, V

Ventricular tachycardia/fibrillation detection algorithm for 24/7 personal wireless heart monitoring

Fokkenrood, S Leijdekkers, P

Gay, V

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, 4541 LNCS pp. 110 - 120
Issue Date:: 2007-12-28

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Field	Value	Language
dc.contributor.author	Fokkenrood, S	en_US
dc.contributor.author	Leijdekkers, P https://orcid.org/0000-0003-3579-1175	en_US
dc.contributor.author	Gay, V https://orcid.org/0000-0002-4301-1995	en_US
dc.date.issued	2007-12-28	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, 4541 LNCS pp. 110 - 120	en_US
dc.identifier.isbn	9783540730347	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1927
dc.description.abstract	This paper describes a Ventricular Tachycardia/Fibrillation (VT/VF) detection algorithm that is specifically designed for a 24/7 personal wireless heart monitoring system. This monitoring system uses Bluetooth enabled biosensors and smart phones to monitor continuously cardiac patients' vital signs. Our VT/VF algorithm is optimized for continuous real-time monitoring on smart phones with a high sensitivity and specificity. We studied and compared existing VT/VF algorithms and selected the one which suited best our requirements. However, we modified and improved the existing algorithm for the smart phone to achieve better performance results. We tested the algorithm on full-length signals from the physionet CU, MIT-db and MIT-vfdb databases [16] without any pre-selection of VT/VF or normal QRS-complex signals. We achieved 97% sensitivity, 98% accuracy and 98% specificity for our implementation which is excellent compared to existing algorithms. © Springer-Verlag Berlin Heidelberg 2007.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Ventricular tachycardia/fibrillation detection algorithm for 24/7 personal wireless heart monitoring	en_US
dc.type	Conference Proceeding
utslib.citation.volume	4541 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Nara, Japan	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	4541 LNCS	en_US

Abstract:

This paper describes a Ventricular Tachycardia/Fibrillation (VT/VF) detection algorithm that is specifically designed for a 24/7 personal wireless heart monitoring system. This monitoring system uses Bluetooth enabled biosensors and smart phones to monitor continuously cardiac patients' vital signs. Our VT/VF algorithm is optimized for continuous real-time monitoring on smart phones with a high sensitivity and specificity. We studied and compared existing VT/VF algorithms and selected the one which suited best our requirements. However, we modified and improved the existing algorithm for the smart phone to achieve better performance results. We tested the algorithm on full-length signals from the physionet CU, MIT-db and MIT-vfdb databases [16] without any pre-selection of VT/VF or normal QRS-complex signals. We achieved 97% sensitivity, 98% accuracy and 98% specificity for our implementation which is excellent compared to existing algorithms. © Springer-Verlag Berlin Heidelberg 2007.

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http://hdl.handle.net/10453/1927