Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders
By Hsin-Tien Chiang, Yi-Yen Hsieh, Szu-Wei Fu, Kuo-Hsuan Hung, Yu Tsao, Shao-Yi Chien. IEEE Access, 2019
308 citations recorded by the source corpus.
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 (SNR $_{\textit{imp}}$ ). 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 SNR $_{\textit{imp}}$ . According to the results, we believe that the proposed FCN-based DAE has a good application prospect in clinical practice.
Subfields: Cardiology and Cardiovascular Medicine, Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine
View Paper: https://www.semanticscholar.org/paper/1161e5997e5b4e974e17ffac6ccf0fab7d9a5565
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In Computer Vision and Pattern Recognition
Citations in the corpus
- Review of noise removal techniques in ECG signals (2020, methodology): “The average reconstruction error of the DAE is as follows: θ∗, θ′∗ = arg min θ, θ′ Eqo X, X~ LH X, gθ′ f θ X ~ (17) where Ep X f X stands for expectation of f X when X has a probability density p X , X is the random variable assigned to x.”