Lightweight Denoising Autoencoder Design for Noise Removal in Electrocardiography

This study proposes two denoising autoencoder (DAE) models with discrete cosine transform (DCT) and discrete wavelet transform (DWT), namely DCT–DAE and DWT–DAE, to remove electrode motion artifacts in noisy electrocardiography (ECG). Initially, the discrete cosine transform and discrete wavelet transform efficiently removed the high-frequency noise. The six encoder layers then retain important ECG features while the six decoder layers reconstruct the clean ECG. To improve the denoising performance, two network layers, the residual block and pixel adjustment, are added to the encoder and decoder layers to solve the vanishing gradient and improve subtle feature extraction. The proposed methods were applied to 66,000 real-recorded noisy ECG fragments. The experimental result indicates that DWT–DAE and DCT–DAE can improve the signal-to-noise ratio by 25.29 and 25.13 dB on average when the input signal-to-noise ratio is -6 dB.