Convolutional Denoising Auto-Encoder Based AWGN Removal From ECG Signal

Abstract

Electrocardiogram (ECG) signal is a non-invasive technique that is currently used to diagnose various types of cardiovascular diseases. However, ECG recording is vulnerable to different types of noises and artifacts that make it very difficult to obtain an accurate diagnosis. In this context, we propose a novel ECG denoising algorithm based on the deep Convolutional Denoising Auto-Encoder (CDAE) which requires minimal preprocessing steps, and conserves the important ECG features. In this study, the proposed CDAE algorithm is specifically implemented to remove the Additive White Gaussian noise (AWGN) from the recorded ECG signal. The CDAE was trained, validated and tested on a set of real ECG signals acquired from the well-known MIT-BIH-Arrythmia (MITDB) database with artificially generated AWGN. The experimental results demonstrate that the proposed method shows better Signal to Noise Ratio (SNR) and lower Root Mean Square Error (RMSE) compared to some of the state-of-the-art methods. The promising results indicate also that the proposed CDAE technique is an effective solution for denoising the ECG signal, by providing ECG waves accentuation for other ECG processing applications like diseases diagnosis.