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Cardiovascular disease diagnosis using cross-domain transfer learning

Abstract:

While cardiovascular diseases (CVDs) are commonly diagnosed by cardiologists via inspecting electrocardiogram (ECG) waveforms, these decisions can be supported by a data-driven approach, which may automate this process. An automatic diagnostic approach often employs hand-crafted features extracted from ECG waveforms. These features, however, do not generalise well, challenged by variation in acquisition settings such as sampling rate and mounting points. Existing deep learning (DL) approaches...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/embc.2019.8857737

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-1552-5630
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Name:
Engineering and Physical Sciences Research Council
Grant:
EP/N027000/1
Publisher:
IEEE
Pages:
4262-4265
Publication date:
2019-10-07
Acceptance date:
2019-05-14
Event title:
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Event location:
Berlin, Germany
Event website:
https://embc.embs.org/2019/
Event start date:
2019-07-23
Event end date:
2019-07-27
DOI:
EISSN:
1558-4615
ISSN:
1557-170X
Pmid:
31946810
EISBN:
9781538613115
ISBN:
9781538613122
Language:
English
Keywords:
Pubs id:
1084790
Local pid:
pubs:1084790
Deposit date:
2020-04-22

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