Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders
Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders
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DOI：
10.1109/access.2019.2912036
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Chien, Shao-Yi
中科院分区：
文献类型：
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作者：
Chiang, Hsin-Tien;Hsieh, Yi-Yen;Chien, Shao-Yi
心电图（ECG）是心律失常检测和预防的有效和无创指标。在实际应用中，心电信号容易受到各种噪声的污染，从而导致错误的解读。因此，如何对心电信号进行去噪处理，以达到准确诊断和分析的目的，已成为人们关注的焦点。可以应用去噪自动编码器（DAE）从其噪声版本重建干净的数据。本文提出了一种基于全卷积网络的心电信号去噪算法。同时，所提出的基于FCN的DAE可以针对DAE体系结构执行压缩。所提出的方法被施加到心电图信号从MIT-BIH心律失常数据库和添加的噪声信号从MIT-BIH噪声应力测试数据库中获得。使用均方根误差（RMSE），平均均方根差（PRD）和信噪比改善（SNRimp）的去噪性能进行评估。对不同输入SNR水平的含噪ECG信号进行的实验结果表明，与基于深度全连接神经网络和卷积神经网络的去噪模型相比，FCN获得了更好的性能。此外，所提出的基于FCN的DAE减小了输入ECG信号的大小，其中压缩数据比原始数据小32倍。研究结果表明，FCN去噪的优越性，具有较低的RMSE和PRD，以及较高的SNRimp。根据实验结果，我们认为基于光纤耦合网络的DAE具有良好的临床应用前景。
The electrocardiogram (ECG) is an efficient and noninvasive indicator for arrhythmia detection and prevention. In real-world scenarios, ECG signals are prone to be contaminated with various noises, which may lead to wrong interpretation. Therefore, significant attention has been paid on denoising of ECG for accurate diagnosis and analysis. A denoising autoencoder (DAE) can be applied to reconstruct the clean data from its noisy version. In this paper, a DAE using the fully convolutional network (FCN) is proposed for ECG signal denoising. Meanwhile, the proposed FCN-based DAE can perform compression with regard to the DAE architecture. The proposed approach is applied to ECG signals from the MIT-BIH Arrhythmia database and the added noise signals are obtained from the MIT-BIH Noise Stress Test database. The denoising performance is evaluated using the root-mean-square error (RMSE), percentage-root-mean-square difference (PRD), and improvement in signal-to-noise ratio (SNRimp). The results of the experiments conducted on noisy ECG signals of different levels of input SNR show that the FCN acquires better performance as compared to the deep fully connected neural network- and convolutional neural network-based denoising models. Moreover, the proposed FCN-based DAE reduces the size of the input ECG signals, where the compressed data is 32 times smaller than the original. The results of the study demonstrate the superiority of FCN in denoising, with lower RMSE and PRD, as well as higher SNRimp. According to the results, we believe that the proposed FCN-based DAE has a good application prospect in clinical practice.