Deep learning with information fusion and model interpretation for long-term prenatal fetal heart rate data-Reference-Cited by-同舟云学术

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Deep learning with information fusion and model interpretation for long-term prenatal fetal heart rate data

Published:2024-09-03 Issue:1 Volume:2 Page:
ISSN:2948-1716
Container-title:npj Women's Health
language:en
Short-container-title:npj Womens Health

Author:

Lin Zenghui,Liu Xintong,Wang Nan,Li Ruichen,Liu Qingao,Ma Jingying,Wang Liwei,Wang Yan,Hong Shenda

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation

Capital’s Funds for Health Improvement and Research

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s44294-024-00033-z.pdf

Reference34 articles.

1. Chandraharan, E. & Arulkumaran, S. Prevention of birth asphyxia: responding appropriately to cardiotocograph (CTG) traces. Best. Pract. Res. Clin. Obstet. Gynaecol. 21, 609–624 (2007).

2. American College of Obstetricians, Gynecologists, et al. Antepartum fetal surveillance: ACOG practice bulletin, number 229. Obstet. Gynecol. 137, e116–e127 (2021).

3. Kapaya, H., Dimelow, E. R. & Anumba, D. Is portable foetal electrocardiogram monitor feasible for foetal heart rate monitoring of small for gestational age foetuses in the home environment. J. Obstet. Gynaecol. 39, 1081–1086 (2019).

4. Pieri, J. et al. Compact long-term recorder for the transabdominal foetal and maternal electrocardiogram. Med. Biol. Eng. Comput. 39, 118–125 (2001).

5. Crawford, A. et al. A mixed-methods evaluation of continuous electronic fetal monitoring for an extended period. Acta Obstet. Gynecol. Scand. 97, 1515–1523 (2018).

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