Detection of Heart Failure Using a Convolutional Neural Network (CNN) via ECG Signals
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-61475-0_37
Reference17 articles.
1. Anbukkarasi, S., Varadhaganapathy, S., Indhiraprakash, P., Jeevanantham, V., Kavin Kumar, G.: Identification of heart disease using machine learning approach. In: 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, pp. 965–970 (2021). https://doi.org/10.1109/ICECA52323.2021.9675865
2. Mohan, S., Thirumalai, C., Srivastava, G.: Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7, 81542–81554 (2019)
3. Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., Singh, P.: Prediction of heart disease using a combination of machine learning and deep learning. Comput. Intell. Neurosci. 2021, 8387680 (2021). https://doi.org/10.1155/2021/8387680
4. Melillo, P., De Luca, N., Bracale, M., Pecchia, L.: Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability. IEEE J. Biomed. Health Inform. 17(3), 727–733 (2013)
5. Guidi, G., Pettenati, M.C., Melillo, P., Iadanza, E.: A machine learning system to improve heart failure patient assistance. IEEE J. Biomed. Health Inform. 18(6), 1750–1756 (2014)
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