Based on wavelet transform denoising and deep learning classification of ECG signals

Tao Liang; Yuan Li

doi:10.1117/12.2687521

28 August 2023 Based on wavelet transform denoising and deep learning classification of ECG signals

Tao Liang, Yuan Li

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 127241B (2023) https://doi.org/10.1117/12.2687521
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

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.

Citation Download Citation

Tao Liang and Yuan Li "Based on wavelet transform denoising and deep learning classification of ECG signals", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 127241B (28 August 2023); https://doi.org/10.1117/12.2687521

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