Artificial Intelligence Advancements in Fetal Monitoring: Enhancing Prenatal Care
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-61625-9_12
Reference12 articles.
1. Warrick, P., Hamilton, E., Precup, D., Kearney, R.: Classification of normal and hypoxic fetuses from systems modeling of intrapartum cardiotocography. IEEE Trans. Biomed. Eng. 57, 771–779 (2010). https://doi.org/10.1109/TBME.2009.2035818
2. Zhao, Z., Zhang, Y., Comert, Z., Deng, Y.: Computer-aided diagnosis system of fetal hypoxia incorporating recurrence plot with convolutional neural network. Front. Physiol. 10 (2019). https://doi.org/10.3389/fphys.2019.00255.
3. Fei, Y., et al.: Intelligent classification of antenatal cardiotocography signals via multimodal bidirectional gated recurrent units. Biomed. Signal Process. Control 78, 104008 (2022). https://doi.org/10.1016/j.bspc.2022.104008
4. Rahman, A., et al.: Fetal ecg extraction from maternal ecg using deeply supervised linknet++ model. Eng. Appl. Artif. Intell. 123, 106414 (2023). https://doi.org/10.1016/j.engappai.2023.106414
5. Krupa, A.J.D., Dhanalakshmi, S., Lai, K.W., Tan, Y., Wu, X.: An iomt enabled deep learning framework for automatic detection of fetal qrs: a solution to remote prenatal care. J. King Saud Univ. Comput. Inf. Sci. 34, 7200–7211 (2022). https://doi.org/10.1016/j.jksuci.2022.07.002
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