Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders

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...

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Vydané v:	IEEE access Ročník 7; s. 60806 - 60813
Hlavní autori:	Chiang, Hsin-Tien, Hsieh, Yi-Yen, Fu, Szu-Wei, Hung, Kuo-Hsuan, Tsao, Yu, Chien, Shao-Yi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Arrhythmia Artificial neural networks Cardiac arrhythmia Cardiac stress tests Convolution Decoding denoising autoencoders Electrocardiography fully convolutional network Neural networks Noise Noise measurement Noise reduction Performance evaluation Root-mean-square errors signal denoising Signal to noise ratio
ISSN:	2169-3536, 2169-3536
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Abstract	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<inline-formula> <tex-math notation="LaTeX">_{\textit{imp}} </tex-math></inline-formula>). 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<inline-formula> <tex-math notation="LaTeX">_{\textit{imp}} </tex-math></inline-formula>. According to the results, we believe that the proposed FCN-based DAE has a good application prospect in clinical practice.
AbstractList	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. 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<inline-formula> <tex-math notation="LaTeX">_{\textit{imp}} </tex-math></inline-formula>). 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<inline-formula> <tex-math notation="LaTeX">_{\textit{imp}} </tex-math></inline-formula>. According to the results, we believe that the proposed FCN-based DAE has a good application prospect in clinical practice.
Author	Hsieh, Yi-Yen Hung, Kuo-Hsuan Tsao, Yu Fu, Szu-Wei Chien, Shao-Yi Chiang, Hsin-Tien
Author_xml	– sequence: 1 givenname: Hsin-Tien surname: Chiang fullname: Chiang, Hsin-Tien organization: Graduate Institute of Electrical Engineering, National Taiwan University, Taipei, Taiwan – sequence: 2 givenname: Yi-Yen surname: Hsieh fullname: Hsieh, Yi-Yen organization: Graduate Institute of Electrical Engineering, National Taiwan University, Taipei, Taiwan – sequence: 3 givenname: Szu-Wei surname: Fu fullname: Fu, Szu-Wei organization: Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan – sequence: 4 givenname: Kuo-Hsuan surname: Hung fullname: Hung, Kuo-Hsuan organization: Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan – sequence: 5 givenname: Yu orcidid: 0000-0001-6956-0418 surname: Tsao fullname: Tsao, Yu email: yu.tsao@citi.sinica.edu.tw organization: Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan – sequence: 6 givenname: Shao-Yi surname: Chien fullname: Chien, Shao-Yi organization: Graduate Institute of Electrical Engineering, National Taiwan University, Taipei, Taiwan
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Snippet	The electrocardiogram (ECG) is an efficient and noninvasive indicator for arrhythmia detection and prevention. In real-world scenarios, ECG signals are prone...
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SubjectTerms	Arrhythmia Artificial neural networks Cardiac arrhythmia Cardiac stress tests Convolution Decoding denoising autoencoders Electrocardiography fully convolutional network Neural networks Noise Noise measurement Noise reduction Performance evaluation Root-mean-square errors signal denoising Signal to noise ratio
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Title	Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders
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