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Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG

Abstract:

The diagnosis of cardiovascular diseases such as atrial fibrillation (AF) is a lengthy and expensive procedure that often requires visual inspection of ECG signals by experts. In order to improve patient management and reduce healthcare costs, automated detection of these pathologies is of utmost importance.


In this study, we classify short segments of ECG into four classes (AF, normal, other rhythms or noise) as part of the Physionet/Computing in Cardiology Challenge 2017. We...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.22489/CinC.2017.360-239

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More from this funder
Name:
Research Councils UK
Funding agency for:
Carr, O
Grant:
EP/G036861/1
More from this funder
Name:
Engineering and Physical Sciences Research Council
Funding agency for:
Andreotti, F
Mahdi, A
Grant:
EP/N024966/1
EP/N024966/1
Publisher:
Institute of Electrical and Electronics Engineers
Host title:
Computing in Cardiology 2017
Journal:
Computing in Cardiology 2017 More from this journal
Volume:
44
Pages:
1-4
Publication date:
2018-04-05
DOI:
EISSN:
2325-887X
ISSN:
2325-8861
Pubs id:
pubs:843774
UUID:
uuid:ac4a6652-af26-420e-8173-340f9f763653
Local pid:
pubs:843774
Source identifiers:
843774
Deposit date:
2018-09-28

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