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Deep learning for continuous electronic fetal monitoring in labor

Abstract:

Continuous electronic fetal monitoring (EFM) is used worldwide to visually assess whether a fetus is exhibiting signs of distress during labor, and may benefit from an emergency operative delivery (e.g. Cesarean section). Previously, computerized EFM assessment that mimics clinical experts showed no benefit in randomized clinical trials. However, as an example of routinely collected ‘big’ data, EFM interpretation should benefit from data-driven computational approaches, such as deep learni...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/EMBC.2018.8513625

Authors


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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Womens & Reproductive Health
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Womens & Reproductive Health
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Womens & Reproductive Health
Department:
Oxford, MSD, Womens & Reproductive Health
Role:
Author
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2018) Journal website
Host title:
40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2018)
Publication date:
2018-10-29
Acceptance date:
2018-05-09
DOI:
Source identifiers:
847091
Pubs id:
pubs:847091
UUID:
uuid:d42c8438-3509-4d51-917d-7737ebb97d0e
Local pid:
pubs:847091
Deposit date:
2018-05-10

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