Predicting high risk birth from real large-scale cardiotocographic data using multi-input convolutional neural networks-Reference-Cited by-同舟云学术

Predicting high risk birth from real large-scale cardiotocographic data using multi-input convolutional neural networks

Published:2021 Issue:3 Volume:12 Page:399-411
ISSN:2185-4106
Container-title:Nonlinear Theory and Its Applications, IEICE
language:en
Short-container-title:NOLTA

Author:

Mohannad Alkanan¹^ORCID,Shibata Chihiro²^ORCID,Miyata Kohei³^ORCID,Imamura Toshiro³^ORCID,Miyamoto Shingo³^ORCID,Fukunishi Hiroaki¹^ORCID,Kameda Hiroyuki¹^ORCID

Affiliation:

1. The Graduate School of Bionics, Computer and Media Science, Tokyo University of Technology

2. Hosei University

3. Department of Obstetrics and Gynecology, Faculty of Medicine, Fukuoka University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Rehabilitation,Physical Therapy, Sports Therapy and Rehabilitation,General Medicine

Link

https://www.jstage.jst.go.jp/article/nolta/12/3/12_399/_pdf

Reference33 articles.

1. [1] Royal college of obstetricians and gynaecologists. each baby counts: 2019 Progress Report, 2020.

2. [2] M. Ahmadpour-Kacho, N. Asnafi, M. Javadian, M. Hajiahmadi, and N. Taleghani, “Correlation between umbilical cord ph and apgar score in high-risk pregnancy,” Iranian journal of pediatrics, vol. 20, no. 4, p. 401, 2010.

3. [3] L. Ali, R. Mushtaq, and N. Ahmed, “Frequency of pathological ctg in low risk women and its outcomes,” Pak J Surg, vol. 30, no. 4, pp. 340-345, 2014.

4. [4] K. Miyata, T. Imamura, S. Miyamoto, H. Fukunishi, A. Mohannad, and C. Shibata, “Application of machine learning techniques to classify fetal hypoxia using japan society of obstetricians and gynecology guidelines,” 2020. (Accepted but not published yet).

5. [5] S. Banu, “Relationship between abnormal cardiotocography and fetal outcome,” Nepal Journal of Obstetrics and Gynaecology, vol. 10, no. 2, pp. 36-39, 2015.

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