Why the study?
The ECG records electrical activity changes during cardiac cycles, making ECG research of important clinical and medical significance.
Does a 10-layer CNN model combined with wavelet transform denoising accurately classify ECG signals?
Does a 10-layer CNN model combined with wavelet transform denoising accurately classify ECG signals?
A deep learning approach using a 10-layer CNN and wavelet transform denoising can effectively classify ECG signals.
Supports CNN-based ECG classification in research settings; hypothesis-generating and should not yet change clinical practice.
The electrocardiogram (ECG) is a graph that records the electrical activity changes of the heart during each cardiac cycle. Therefore, it is of important clinical and medical significance to pay attention to ECG research. In this paper, we selected the appropriate parameters such as wavelet basis, threshold and the number of decomposition, and the different noises in the ECG signal were filtered by wavelet soft threshold transformation. Then, a 10 layer CNN model was used to classify and recognize the preprocessed ECG signals. Experiments on the MIT-BIH database verified that this model had a good classification effect.
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Tao et al. (2023) studied this question.
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