ECG Quality Assessment Using Deep Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-52382-3_21
Reference15 articles.
1. Kiranyaz, S., Ince, T., Gabbouj, M.: Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans. Biomed. Eng. 63(3), 664–675 (2016). https://doi.org/10.1109/TBME.2015.2468589
2. Brüser, C., Antink, C.H., Wartzek, T., Walter, M., Leonhardt, S.: Ambient and unobtrusive cardiorespiratory monitoring techniques. IEEE Rev. Biomed. Eng. 8, 30–43 (2015). https://doi.org/10.1109/RBME.2015.2414661
3. Satija, U., Ramkumar, B., Manikandan, M.S.: A review of signal processing techniques for electrocardiogram signal quality assessment. IEEE Rev. Biomed. Eng. 11, 36–52 (2018). https://doi.org/10.1109/RBME.2018.2810957
4. Zheng, X., Niu, T., Li, X., Zhang, Y.: ECGbeat classification using a 2-D convolutional neural network. Biomed. Signal Process. Control 32, 1–8 (2017)
5. Giridhar, K., Devisetty, U.K.: ECG beat classification using deep learning techniques. arXiv preprint arXiv:1806.00909 (2018)
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