Research On ECG Signal Preprocessing Based On Generative Adversarial Network

With the improvement of medical level in recent years,more and more people are paying attention to the protection of cardiovascular diseases,and electrocardiogram signals reflect the process of changes in cardiac electrical activity,which is an important way to diagnose heart related diseases.However,the waveform of electrocardiogram signals is complex,and the collected signals often contain noise,which make it difficult to identify heart disease types.In response to the above issues,this article will construct a Fusing Wavelet Transform and Generating Adversarial Network based on Fully Convolutional Denoising Autoencoder(WT-FCDAE-GAN).This model will be applied to the preprocessing of electrocardiogram signals,and this method will be further studied and analyzed.A Generating Adversarial Network based on Fully Convolutional Denoising Autoencoder(FCDAE-GAN)was constructed,which consists of a generator and a discriminator.The model is optimized through alternating iterative training,with the generator mainly using a FCDAE.Solve the problem of losing waveform details when using traditional denoising methods for electrocardiogram signals.Different loss function are designed to compare the difference of network performance.When different noises are added,the signal reconstruction ability of the model is discussed.The feasibility of the proposed model and the effectiveness of the reconstructed signal are verified by using MIT-BIH ECG data set.A WT-FCDAE-GAN deep learning network model was constructed,which fused wavelet transform with FCDAE-GAN model to further improve the accuracy of signal reconstruction under composite noise conditions.The introduction of wavelet transform first preprocesses the data of the FCDAE-GAN model,and overlays it with the original signal in a feedforward manner to enhance signal features and improve signal-to-noise ratio.Then,more deep features are obtained through the FCDAE-GAN model to reconstruct the original clean signal with higher accuracy.Compared to the FCDAEGAN model,the signal-to-noise ratio of the WT-FCDAE-GAN model is further improved.A multi lead remote electrocardiogram signal acquisition system has been designed.By designing a modular collection device,the collection module obtains the collected data,then transmits the data to the main control module,and finally uploads the electrocardiogram data to the client.By implementing the collection of user electrocardiogram data,registering and logging in the display interface,and viewing the effectiveness of collecting electrocardiogram data,the practicality of the collection platform has been verified.