Abstract
High-quality and high-fidelity removal of noise in the Electrocardiogram (ECG) signal is of great significance to the auxiliary diagnosis of ECG diseases. In view of the single function of traditional denoising methods and the insufficient performance of signal details after denoising, a new method of ECG denoising based on the combination of the Generative Adversarial Network (GAN) and Residual Network is proposed. The method adopted in this paper is based on the GAN structure, and it restructures the generator and discriminator. In the generator network, residual blocks and Skip-Connecting are used to deepen the network structure and better capture the in-depth information in the ECG signal. In the discriminator network, the ResNet framework is used. In order to optimize the noise reduction process and solve the lack of local relevance considering the global ECG problem, the differential function and overall function of the maximum local difference are added in the loss function in this paper. The experimental results prove that the method used in this article has better performance than the current excellent S-Transform (S-T) algorithm, Wavelet Transform (WT) algorithm, Stacked Denoising Autoencoder (S-DAE) algorithm, and Improved Denoising Autoencoder (I-DAE) algorithm. Experiments show that the Root Mean Square Error (RMSE) of this method in the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) noise pressure database is 0.0102, and the Signal-to-Noise Ratio (SNR) is 40.8526 dB, which is compared with that of the most advanced experimental methods. Our method improves the SNR by 88.57% on average. Besides the three noise intensities for comparison experiments, additional noise reduction experiments are also performed under four noise intensities in our paper. The experimental results verify the scientific nature of the model, which is that our method can effectively retain the important information conveyed by the original signal.
Highlights
As one of the main components of cardiovascular diseases, heart disease is extremely harmful, affecting patients’ normal life, and can be fatal
The first row is the noisy ECG signal, the second row is the original clean ECG signal, and the third row is the clean signal after noise reduction
For a more intuitive comparison, we stack the effect pictures together, as shown in the fourth row of effect pictures. It can be seen from the figure that the denoised ECG signal can coincide well with the original ECG signal. These results show that our method has a very good effect on removing single noise
Summary
As one of the main components of cardiovascular diseases, heart disease is extremely harmful, affecting patients’ normal life, and can be fatal. Electrocardiogram (ECG) is one of the main techniques for heart disease diagnosis [1, 2], which mainly reflects the electrical activity of the heart. Doctors can make a quick judgment on the heart condition by observing the shape, amplitude, and interval of continuous heartbeats of the waveform. This is the most effective and quickest method of monitoring [3, 4], classification [5, 6], and treatment of heart diseases. We find that the collected ECG signal is often mixed with a lot of noise, which is not conducive to signal analysis. The most important step in data processing is to denoise the collected signal so as to improve the usability of the signal
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