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Detection of Atrial Fibrillation From Short ECGs: Minimalistic Complexity Analysis for Feature-Based Classifiers
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.ORCID-id: 0000-0002-1313-0934
2018 (engelsk)Inngår i: 2018 Computing in Cardiology Conference (CinC), IEEE, 2018Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In order to facilitate data-driven solutions for early detection of atrial fibrillation (AF), the 2017 CinC conference challenge was devoted to automatic AF classification based on short ECG recordings. The proposed solutions concentrated on maximizing the classifiers F-1 score, whereas the complexity of the classifiers was not considered. However, we argue that this must be addressed as complexity places restrictions on the applicability of inexpensive devices for AF monitoring outside hospitals. Therefore, this study investigates the feasibility of complexity reduction by analyzing one of the solutions presented for the challenge.

sted, utgiver, år, opplag, sider
IEEE, 2018.
Serie
Computing in Cardiology Conference, ISSN 2325-8861
HSV kategori
Identifikatorer
URN: urn:nbn:se:umu:diva-166998DOI: 10.22489/CinC.2018.147ISI: 000482598700249Scopus ID: 2-s2.0-85068742924ISBN: 978-1-7281-0958-9 (tryckt)OAI: oai:DiVA.org:umu-166998DiVA, id: diva2:1384019
Konferanse
45th Computing in Cardiology Conference (CinC), SEP 23-26, 2018, Maastricht, NETHERLANDS
Tilgjengelig fra: 2020-01-09 Laget: 2020-01-09 Sist oppdatert: 2020-01-09bibliografisk kontrollert

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