Cleaning ECG with Deep Learning: A Denoiser Based on Gated Recurrent Units
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-36007-7_11
Reference19 articles.
1. Li, H.Z., Boulanger, P.: A survey of heart anomaly detection using ambulatory electrocardiogram (ECG). Sensors 20(5), 1461 (2020). https://doi.org/10.3390/S20051461
2. Joshi, S.L., Vatti, R.A., Tornekar, R.V.: A survey on ECG signal denoising techniques. In: Proceedings - 2013 International Conference on Communication Systems and Network Technologies, CSNT 2013, pp. 60–64 (2013). https://doi.org/10.1109/CSNT.2013.22
3. Chatterjee, S., Thakur, R.S., Yadav, R.N., Gupta, L., Raghuvanshi, D.K.: Review of noise removal techniques in ECG signals. IET Signal Process. 14(9), 569–590 (2020). https://doi.org/10.1049/IET-SPR.2020.0104
4. Wang, J., et al.: Adversarial de-noising of electrocardiogram. Neurocomputing 349, 212–224 (2019). https://doi.org/10.1016/J.NEUCOM.2019.03.083
5. Liu, X., Wang, H., Li, Z., Qin, L.: Deep learning in ECG diagnosis: a review. Knowl.-Based Syst. 227, 107187 (2021). https://doi.org/10.1016/j.knosys.2021.107187
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