Explanations of Augmentation Methods for Deep Learning ECG Classification
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-66535-6_30
Reference20 articles.
1. Chen, S.W., Wang, S.L., Qi, X.Z., Samuri, S.M., Yang, C.: Review of ECG detection and classification based on deep learning: coherent taxonomy, motivation, open challenges and recommendations. Biomed. Signal Process. Control 74, 103493 (2022)
2. Dakshit, S., Prabhakaran, B.: CVAE-based generator for variable length synthetic ECG. In: 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI), pp. 235–244. IEEE (2023)
3. Dakshit, S., Prabhakaran, B.: Twelve lead double stacked generalization for ECG classification. In: 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI), pp. 245–251. IEEE (2023)
4. Do, E., Boynton, J., Lee, B.S., Lustgarten, D.: Data augmentation for 12-lead ECG beat classification. SN Comput. Sci. 3, 1–17 (2022)
5. Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)
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