A CNN-RNN unified framework for intrapartum cardiotocograph classification-Reference-Cited by-同舟云学术

A CNN-RNN unified framework for intrapartum cardiotocograph classification

Published:2023-02 Issue: Volume:229 Page:107300
ISSN:0169-2607
Container-title:Computer Methods and Programs in Biomedicine
language:en
Short-container-title:Computer Methods and Programs in Biomedicine

Author:

Liang Huanwen,Lu Yu

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications,Software

Reference48 articles.

1. Prevention of birth asphyxia: Responding appropriately to cardiotocograph (CTG) traces;Chandraharan;Best Pract. Res. Clin. Obstet. Gynaecol.,2007

2. How does electronic fetal heart rate monitoring affect labor and delivery outcomes?[J];Barstow;J. Fam. Pract.,2010

3. FIGO consensus guidelines on intrapartum fetal monitoring: Physiology of fetal oxygenation and the main goals of intrapartum fetal monitoring;Ayres-de-Campos;Int. J. Gynecol. Obstetr. Official Organ. Inter. Federation of Gynaecol. Obstetrics,2015

4. Intrapartum fetal surveillance;Talaulikar;Obstet. Gynaecol. Reprod. Med.,2014

5. Deep learning;LeCun;Nature,2015

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