DeepCEDNet: An Efficient Deep Convolutional Encoder-Decoder Networks for ECG Signal Enhancement

Electrocardiogram (ECG) signal can be thought of as an effective indicator for detection of various arrhythmias. However, the acquired ECG data is always corrupted by amounts of noise, which have a great influence on the diagnosis of cardiovascular diseases. In this paper, an efficient deep convolutional encoder-decoder network framework is proposed to remove the noise from ECG signal, which is termed as `DeepCEDNet'. This network is able to learn a sparse representation of data in the time-frequency domain via the high-order synchrosqueezing transform (FSSTH) and a nonlinear function that maps the noisy data into the clean one based on the distribution difference between signal and noise from the training set. Extensive experiments are conducted on ECG signals from the MIT-BIH Arrhythmia database and MIT-BIH Long-Term ECG database, and the added noise is obtained from the MIT-BIH Noise Stress Test database. The denoising performance is evaluated by means of signal to noise ratio (SNR), root mean squared error (RMSE) and percent root mean square difference (PRD). The results indicate that the proposed DeepCEDNet can obtain superior performance in both noise reduction and details preservation with higher SNR and lower RMSE and PRD compared to the traditional convolutional neural network (CNN) and the fully convolutional network-based denoising auto-encoder (FCN). We believe that the DeepCEDNet has a wide application prospect in the biomedical field.