ECG signal enhancement based on improved denoising auto-encoder

The electrocardiogram (ECG) is a primary diagnostic tool for examining cardiac tissue and structures. ECG signals are often contaminated by noise, which can manifest with similar morphologies as an ECG waveform in the frequency domain. In this paper, a novel deep neural network (DNN) is proposed to solve the above mentioned problem. This DNN is created from an improved denoising auto-encoder (DAE) reformed by a wavelet transform (WT) method. A WT with scale-adaptive thresholding method is used to filter most of the noise. A DNN based on improved DAE is then used to remove any residual noise, which is often complex with an unknown distribution in the frequency domain. The proposed method was evaluated on ECG signals from the MIT-BIH Arrhythmia database, and added noise signals were obtained from the MIT-BIH Noise Stress Test database. The results show that the average of output signal-to-noise ratio (SNR) is from 21.56 dB to 22.96 dB, and the average of root mean square error (RMSE) is less than 0.037. The proposed method showed significant improvement in SNR and RMSE compared with the individual processing with either a WT or DAE, thus providing promising approaches for ECG signal enhancement.