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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
Version:
Accepted manuscript

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Publisher copy:
10.1109/EMBC.2018.8513625

Authors


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
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Womens & Reproductive Health
Role:
Author
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Publication date:
2018-10-29
Acceptance date:
2018-05-09
DOI:
Pubs id:
pubs:847091
URN:
uri:d42c8438-3509-4d51-917d-7737ebb97d0e
UUID:
uuid:d42c8438-3509-4d51-917d-7737ebb97d0e
Local pid:
pubs:847091

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