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

Pingping Bing,Zhihua Zhang,Wei Liu

doi:10.1109/access.2021.3072640

Pingping Bing, Zhihua Zhang + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3072640

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Abstract

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.

Highlights

Recorded electrocardiogram (ECG) signal is inevitably contaminated by various types of noise [1], [2], such as baseline wander (BW), muscle artifact (MA), and electrode motion (EM), and so on
The key idea of the methods based on principle component analysis (PCA) and independent component analysis (ICA) is to eliminate the dimensions corresponding to noise, the obtained mapping model is more sensitive to small disturbances in the signal or noise
Experimental results show that DeepCEDNet performs clearly better in noise removal and details preservation compared with the traditional convolutional neural network (CNN) and fully convolutional network-based denoising auto-encoder (FCN), which can be attributed to three aspects: (1) the difference between signal and noise is more obvious in the time-frequency domain; (2) DeepCEDNet makes full use of the capability of auto-encoders in learning a sparse representation of data; (3) the skip connections between two corresponding convolutional and deconvolutional layers help to handle the problem of gradient vanishing, improve the signal reconstruction performance, and enhance the robustness of deep neural network

Summary

Introduction

Recorded electrocardiogram (ECG) signal is inevitably contaminated by various types of noise (coined artifacts) [1], [2], such as baseline wander (BW), muscle artifact (MA), and electrode motion (EM), and so on. The noise removal from ECG signal is becoming urgent [7], [8]. Numerous efforts have been made to develop different methods for denoising ECG signal, for example adaptive filter [9]–[11], wavelet transform [12], The associate editor coordinating the review of this manuscript and approving it for publication was Chih-Yu Hsu. principle component analysis (PCA) [13], independent component analysis (ICA) [14], and empirical mode decomposition (EMD) [15], [16]. The key idea of the methods based on PCA and ICA is to eliminate the dimensions corresponding to noise, the obtained mapping model is more sensitive to small disturbances in the signal or noise. EMD-based approaches decompose the noisy signal into a series of intrinsic mode

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