Recurrent Neural Network Based Classification of Fetal Heart Rate Using Cardiotocograph

Author:

Das Sahana,Mukherjee Himadri,Obaidullah Sk. Md.,Santosh K. C.,Roy Kaushik,Saha Chanchal Kumar

Publisher

Springer Singapore

Reference17 articles.

1. Yilmaz, E., Kilikçier, C.: Determination of fetal state from cardiotocogram using LSSVM with particle swarm optimization and binary decision tree. J. Comput. Math. Methods Med. 2013, 1–8 (2013)

2. Das, S., Roy, K., Saha, C.K.: A linear time series analysis of fetal heart rate to detect the variability: measures using cardiotocography. In: Bhattacharyya, S., Das, N., Bhattacharyya, D., Mukherjee, A. (eds.) Handbook of Research on Recent Developments in Intelligent Communication Application, vol. 1, pp. 471–495. IGI Global (2017)

3. Das, S., Roy, K., Saha, C.K.: Application of FURIA in the classification of cardiotocograph. In: IEEE International Conference on Research and Development Prospects on Engineering and Technology, pp. 120–124. IEEE Press, Chennai (2013)

4. De-Campos, A., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring. Int. J. Gynecol. Obstet. 131(1), 13–24 (2015)

5. Macones, G.A., et al.: The 2008 National Institute of Child Health and Human Development workshop report on electronic fetal monitoring: update on definitions, interpretation, and research guidelines. J. Am. Coll. Obstet. Gynecol. 112, 661–666 (2008)

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A CNN-RNN unified framework for intrapartum cardiotocograph classification;Computer Methods and Programs in Biomedicine;2023-02

2. Fully Automatic Classification of Cardiotocographic Signals with 1D-CNN and Bi-directional GRU;2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);2022-07-11

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