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Detection of Atrial...
Detection of Atrial Fibrillation from Short ECGs : Minimalistic Complexity Analysis for Feature-Based Classifiers
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- Abdukalikova, Anara (författare)
- Luleå tekniska universitet,Datavetenskap
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- Kleyko, Denis, 1990- (författare)
- Luleå tekniska universitet,Datavetenskap
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- Osipov, Evgeny (författare)
- Luleå tekniska universitet,Datavetenskap
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- Wiklund, Urban (författare)
- Umeå universitet,Radiofysik,Umeå University, Umeå, Sweden
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(creator_code:org_t)
- IEEE, 2018
- 2018
- Engelska.
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Ingår i: Computing in Cardiology 2018. - : IEEE.
- Relaterad länk:
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https://doi.org/10.2...
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https://umu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.2...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Nyckelord
- Dependable Communication and Computation Systems
- Kommunikations- och beräkningssystem
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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