Exploring Effective Approaches on Transformer-Based Neural Models for Multi-clinical Large-Scale Cardiotocogram Data
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62502-2_51
Reference11 articles.
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2. Mohannad, A., et al.: Predicting high risk birth from real large-scale cardiotocographic data using multi-input convolutional neural networks. Nonlinear Theor. Appl. IEICE 12(3), 399–411 (2019). https://doi.org/10.1587/nolta.12.399
3. Zhao, Z., Deng, Y., Zhang, Y., Zhang, Y., Zhang, X., Shao, L.: DeepFHR: Intelligent prediction of fetal acidemia using fetal heart rate signals based on convolutional neural network. BMC Med. Inform. Decis. Mak. 19(1), 286 (2019). https://doi.org/10.1186/s12911-019-1007-5
4. Ma’sum, M.A., Dewi Intan, P.R., Jatmiko, W., Krisnadhi, A.A., Setiawan, N.A., Suarjaya, I.M.A.D.: Improving deep learning classifier for fetus hypoxia detection in cardiotocography signal. In: 2019 International Workshop on Big Data and Information Security (IWBIS), Bali, Indonesia, pp. 51–56. IEEE (2019). https://doi.org/10.1109/IWBIS.2019.8935835
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