Development and Implementation of an Efficient Deep Residual Network for ECG Classification
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-7524-0_24
Reference21 articles.
1. Wang, D., Si, Y., Yang, W., Zhang, G., Liu, T.: A novel heart rate robust method for short-term electrocardiogram biometric identification. Appl. Sci. 9(1), 201 (2019)
2. Lu, W., Hou, H., Chu, J.: Feature fusion for imbalanced ECG data analysis. Biomed. Signal Process. Control 41, 152–160 (2018)
3. Raj, S., Ray, K.C.: ECG signal analysis using DCT-Based DOST and PSO optimized SVM. IEEE Trans. Instrum. Meas. 66(3), 470–478 (2017)
4. Varatharajan, R., Manogaran, G., Priyan, M.: A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimedia Tools Appl. 77(8), 10195–10215 (2018)
5. Zihlmann, M., Perekrestenko, D., Tschannen, M.: Convolutional recurrent neural networks for electrocardiogram classification. In: 2017 Computing in Cardiology (CinC), pp. 1–4 (2017)
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