Abstract
The electrocardiogram (ECG) has been proved to be the most common and effective method of studying cardiovascular disease because it is simple, non-invasive, and inexpensive. However, the differences between ECG signals are difficult to distinguish. In this paper, a model combining convolutional neural networks (CNN) with self-discipline learning (SDL) is proposed to realize the classification and identification of cardiac arrhythmia data. Comparison with a variety of deep learning frameworks based on the MIT-BIH arrhythmia dataset shows that, this model achieves a higher level of accuracy with less structure.
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Gu, Z., Sun, X., Sun, Y. (2023). The Self-discipline Learning Model with Imported Backpropagation Algorithm. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-031-16072-1_57
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DOI: https://doi.org/10.1007/978-3-031-16072-1_57
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