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Comparison of publicly available beat detection algorithms performances on the ECGs obtained by a patch ECG device
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-2457-3079
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-1940-1747
Jožef Stefan Institute, Department of Communication Systems, Ljubljana, Slovenia.
2019 (English)In: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 275-278Conference paper, Published paper (Refereed)
Abstract [en]

Eight ECG beat detection algorithms, from the PhysioNet's WFDB and Cardiovascular Signal toolboxes, were tested on twenty measurements, obtained by the Savvy patch ECG device, for their accuracy in beat detection. On each subject, one measurement is obtained while sitting and one while running. Each measurement lasted from thirty seconds to one minute. The measurements obtained while running were more challenging for all the algorithms, as most of them almost perfectly detected all the beats on the measurements obtained in sitting position. However, when applied on the measurements obtained while running, all the algorithms have performed with decreased accuracy. Considering overall percentage of the faulty detected peaks, the four best algorithms were jqrs, from the Cardiovascular Signal Toolbox, and ecgpuwave, gqrs, and wqrs, from the WFDB Toolbox, with percentages of faulty detected beats 1.7, 2.3, 2.9, and 3, respectively. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019. p. 275-278
Keywords [en]
Beat detection, Heart rate, Patch ECG, R-peaks, Remote health monitoring, Telemonitoring, Electrocardiography, Microelectronics, Telemedicine, Heart rates, Tele-monitoring, Signal detection
National Category
Other Medical Engineering Medical Laboratory and Measurements Technologies Signal Processing Medical Equipment Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-45016DOI: 10.23919/MIPRO.2019.8756769Scopus ID: 2-s2.0-85070300696ISBN: 9789532330984 (print)OAI: oai:DiVA.org:mdh-45016DiVA, id: diva2:1343081
Conference
42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019, 20 May 2019 through 24 May 2019
Available from: 2019-08-15 Created: 2019-08-15 Last updated: 2019-08-15Bibliographically approved

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Tomasic, IvanPetrovic, NikolaLindén, Maria

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