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Detection of Atrial Fibrillation From Short ECGs: Minimalistic Complexity Analysis for Feature-Based Classifiers
Umeå University, Faculty of Medicine, Department of Radiation Sciences.ORCID iD: 0000-0002-1313-0934
2018 (English)In: 2018 Computing in Cardiology Conference (CinC), IEEE, 2018Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2018.
Series
Computing in Cardiology Conference, ISSN 2325-8861
National Category
Cardiac and Cardiovascular Systems
Identifiers
URN: urn:nbn:se:umu:diva-166998DOI: 10.22489/CinC.2018.147ISI: 000482598700249Scopus ID: 2-s2.0-85068742924ISBN: 978-1-7281-0958-9 (print)OAI: oai:DiVA.org:umu-166998DiVA, id: diva2:1384019
Conference
45th Computing in Cardiology Conference (CinC), SEP 23-26, 2018, Maastricht, NETHERLANDS
Available from: 2020-01-09 Created: 2020-01-09 Last updated: 2020-01-09Bibliographically approved

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Wiklund, Urban

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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