Author:
Lu Yu,Liang Huanwen,Yu Zichang,Fu Xianghua
Publisher
Springer Nature Singapore
Reference20 articles.
1. Asfaw, D., Jordanov, I., Impey, L., Namburte, A., Lee, R., Georgieva, A.: Multimodal deep learning for predicting adverse birth outcomes based on early labour data. Bioengineering 10(6), 730 (2023)
2. Baghel, N., Burget, R., Dutta, M.K.: 1D-FHRNet: automatic diagnosis of fetal acidosis from fetal heart rate signals. Biomed. Signal Process. Control 71, 102794 (2022)
3. Boudet, S., Houzé de l’Aulnoit, A., Peyrodie, L., Demailly, R., Houzé de l’Aulnoit, D.: Use of deep learning to detect the maternal heart rate and false signals on fetal heart rate recordings. Biosensors 12(9), 691 (2022)
4. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
5. Cui, Z., Chen, W., Chen, Y.: Multi-scale convolutional neural networks for time series classification. arXiv preprint arXiv:1603.06995 (2016)
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