ECG SIGNAL DENOISING BASED ON DEEP FULLY CONVOLUTIONAL BOOSTING NETWORK
The traditional method of denoise is difficult to accurately remove the complex noise without losing the ECG signal,an ECG signal denoising method is proposed based on the deep full convolutional Boosting network(FCBN).This method used the characteristics of the local connection of the full convolutional network to retain the detailed information of the ECG signal waveform,and stacked multiple FCN networks through the Boosting algorithm to form a deep neural network.The original signal in multiple stages were inputted,and it retained the deep information of the ECG signal features to improve the denoising performance of the overall network.Experimental results show that compared with wavelet threshold method,S transform method,BPNN method and convolutional autoencoder,this method has obvious improvement in SNR and smaller RMSE,while retaining more ECG signal waveforms morphological information.
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