ECG Signal Denoising Method Based on Disentangled Autoencoder

The electrocardiogram (ECG) is widely used in medicine because it can provide basic information about different types of heart disease. However, ECG data are usually disturbed by various types of noise, which can lead to errors in diagnosis by doctors. To address this problem, this study proposes a...

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Published in:	Electronics (Basel) Vol. 12; no. 7; p. 1606
Main Authors:	Lin, Haicai, Liu, Ruixia, Liu, Zhaoyang
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.04.2023
Subjects:	Algorithms Artificial neural networks Cardiovascular disease Coding Deep learning Diagnosis Electrocardiogram Electrocardiography Electrodes Heart Heart diseases Methods Neural networks Noise Noise reduction Scientific validity Signal processing Signal to noise ratio Wavelet transforms China
ISSN:	2079-9292, 2079-9292
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Abstract	The electrocardiogram (ECG) is widely used in medicine because it can provide basic information about different types of heart disease. However, ECG data are usually disturbed by various types of noise, which can lead to errors in diagnosis by doctors. To address this problem, this study proposes a method for denoising ECG based on disentangled autoencoders. A disentangled autoencoder is an improved autoencoder suitable for denoising ECG data. In our proposed method, we use a disentangled autoencoder model based on a fully convolutional neural network to effectively separate the clean ECG data from the noise. Unlike conventional autoencoders, we disentangle the features of the coding hidden layer to separate the signal-coding features from the noise-coding features. We performed simulation experiments on the MIT-BIH Arrhythmia Database and found that the algorithm had better noise reduction results when dealing with four different types of noise. In particular, using our method, the average improved signal-to-noise ratios for the three noises in the MIT-BIH Noise Stress Test Database were 27.45 db for baseline wander, 25.72 db for muscle artefacts, and 29.91 db for electrode motion artefacts. Compared to a denoising autoencoder based on a fully convolutional neural network (FCN), the signal-to-noise ratio was improved by an average of 12.57%. We can conclude that the model has scientific validity. At the same time, our noise reduction method can effectively remove noise while preserving the important information conveyed by the original signal.
AbstractList	The electrocardiogram (ECG) is widely used in medicine because it can provide basic information about different types of heart disease. However, ECG data are usually disturbed by various types of noise, which can lead to errors in diagnosis by doctors. To address this problem, this study proposes a method for denoising ECG based on disentangled autoencoders. A disentangled autoencoder is an improved autoencoder suitable for denoising ECG data. In our proposed method, we use a disentangled autoencoder model based on a fully convolutional neural network to effectively separate the clean ECG data from the noise. Unlike conventional autoencoders, we disentangle the features of the coding hidden layer to separate the signal-coding features from the noise-coding features. We performed simulation experiments on the MIT-BIH Arrhythmia Database and found that the algorithm had better noise reduction results when dealing with four different types of noise. In particular, using our method, the average improved signal-to-noise ratios for the three noises in the MIT-BIH Noise Stress Test Database were 27.45 db for baseline wander, 25.72 db for muscle artefacts, and 29.91 db for electrode motion artefacts. Compared to a denoising autoencoder based on a fully convolutional neural network (FCN), the signal-to-noise ratio was improved by an average of 12.57%. We can conclude that the model has scientific validity. At the same time, our noise reduction method can effectively remove noise while preserving the important information conveyed by the original signal.
Audience	Academic
Author	Lin, Haicai Liu, Ruixia Liu, Zhaoyang
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Cites_doi	10.37896/YMER21.08/36 10.1109/ICCIC.2010.5705839 10.1016/j.bspc.2018.05.002 10.1109/ACCESS.2021.3072640 10.14257/ijbsbt.2016.8.4.05 10.1016/j.engappai.2016.02.015 10.1109/ICASSP.2017.7953017 10.3991/ijoe.v16i09.14485 10.1016/j.measurement.2018.05.033 10.22489/CinC.2019.015 10.3390/app12178839 10.1109/SMC.2014.6974120 10.1049/htl.2015.0029 10.1088/0967-3334/26/5/R01 10.1016/j.jacc.2020.11.010 10.1109/TCBB.2020.2976981 10.1109/ACCESS.2019.2912036 10.1016/j.irbm.2013.07.012 10.1109/JSEN.2013.2257742 10.1016/j.bspc.2013.06.005 10.1038/s41598-018-35053-8 10.1038/s41598-019-42516-z 10.1109/FABS52071.2021.9702655 10.1016/j.jelectrocard.2003.09.038 10.1088/0967-3334/37/12/2093 10.1016/j.probengmech.2015.10.005 10.1007/BF02351029
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References	Ning (ref_20) 2013; 8 Fan (ref_33) 2016; 43 Syed (ref_3) 2005; 43 Tan (ref_17) 2021; 45 ref_36 ref_12 Roth (ref_1) 2020; 76 Costa (ref_29) 2003; 36 ref_32 ref_31 Sun (ref_34) 2018; 8 Bengio (ref_28) 2014; 35 Sarangi (ref_6) 2022; 21 Sharma (ref_10) 2018; 45 ref_16 Hammad (ref_5) 2018; 125 Singh (ref_26) 2021; 18 Goel (ref_14) 2016; 8 Zhou (ref_19) 2016; 37 Das (ref_18) 2013; 34 Xiong (ref_22) 2016; 52 ref_25 ref_24 ref_23 Bing (ref_15) 2021; 9 ref_21 Chiang (ref_30) 2019; 7 Labate (ref_11) 2013; 13 Addison (ref_13) 2005; 26 Zhu (ref_27) 2019; 9 Moridani (ref_2) 2020; 16 Gupta (ref_9) 2015; 2 Duchi (ref_35) 2011; 12 ref_8 ref_4 ref_7
References_xml	– volume: 21 start-page: 400 year: 2022 ident: ref_6 article-title: Singular Spectrum Analysis Based EMG Artifact Removal from ECG Signal publication-title: YMER Digit. doi: 10.37896/YMER21.08/36 – ident: ref_16 doi: 10.1109/ICCIC.2010.5705839 – ident: ref_32 – volume: 45 start-page: 33 year: 2018 ident: ref_10 article-title: Baseline wander and power line interference removal from ECG signals using eigenvalue decomposition publication-title: Biomed. Signal Process. Control. doi: 10.1016/j.bspc.2018.05.002 – volume: 9 start-page: 56699 year: 2021 ident: ref_15 article-title: DeepCEDNet: An Efficient Deep Convolutional Encoder-Decoder Networks for ECG Signal Enhancement publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3072640 – ident: ref_24 – volume: 8 start-page: 39 year: 2016 ident: ref_14 article-title: An Optimal Wavelet Approach for ECG Noise Cancellation publication-title: Int. J. Bio-Sci. Bio-Technol. doi: 10.14257/ijbsbt.2016.8.4.05 – volume: 52 start-page: 194 year: 2016 ident: ref_22 article-title: ECG signal enhancement based on improved denoising auto-encoder publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2016.02.015 – ident: ref_21 doi: 10.1109/ICASSP.2017.7953017 – volume: 16 start-page: 137 year: 2020 ident: ref_2 article-title: A Review of the Methods for Sudden Cardiac Death Detection: A Guide for Emergency Physicians publication-title: Int. J. Online Biomed. Eng. doi: 10.3991/ijoe.v16i09.14485 – volume: 125 start-page: 634 year: 2018 ident: ref_5 article-title: Detection of abnormal heart conditions based on characteristics of ECG signals publication-title: Measurement doi: 10.1016/j.measurement.2018.05.033 – ident: ref_23 doi: 10.22489/CinC.2019.015 – ident: ref_8 doi: 10.3390/app12178839 – ident: ref_7 doi: 10.1109/SMC.2014.6974120 – volume: 2 start-page: 164 year: 2015 ident: ref_9 article-title: Baseline wander removal of electrocardiogram signals using multivariate empirical mode decomposition publication-title: Health Technol. Lett. doi: 10.1049/htl.2015.0029 – volume: 26 start-page: R155 year: 2005 ident: ref_13 article-title: Wavelet transforms and the ECG: A review publication-title: Physiol. Meas. doi: 10.1088/0967-3334/26/5/R01 – volume: 76 start-page: 2982 year: 2020 ident: ref_1 article-title: Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study publication-title: J. Am. Coll. Cardiol. doi: 10.1016/j.jacc.2020.11.010 – volume: 18 start-page: 759 year: 2021 ident: ref_26 article-title: A New ECG Denoising Framework Using Generative Adversarial Network publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. doi: 10.1109/TCBB.2020.2976981 – volume: 45 start-page: 1 year: 2021 ident: ref_17 article-title: Application of improved wavelet threshold in ECG signal de-noising publication-title: Chin. J. Med. Devices – volume: 7 start-page: 60806 year: 2019 ident: ref_30 article-title: Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2912036 – ident: ref_4 – ident: ref_31 – ident: ref_12 – volume: 34 start-page: 362 year: 2013 ident: ref_18 article-title: Analysis of ECG signal denoising method based on S-transform publication-title: IRBM doi: 10.1016/j.irbm.2013.07.012 – volume: 13 start-page: 2666 year: 2013 ident: ref_11 article-title: Empirical Mode Decomposition vs. Wavelet Decomposition for the Extraction of Respiratory Signal from Single-Channel ECG: A Comparison publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2013.2257742 – volume: 8 start-page: 713 year: 2013 ident: ref_20 article-title: ECG Enhancement and QRS Detection Based on Sparse Derivatives publication-title: Biomed. Signal Process. Control. doi: 10.1016/j.bspc.2013.06.005 – volume: 35 start-page: 1798 year: 2014 ident: ref_28 article-title: Representation Learning: A Review and New Perspectives publication-title: arXiv – volume: 8 start-page: 17211 year: 2018 ident: ref_34 article-title: Root Mean Square Minimum Distance as a Quality Metric for Stochastic Optical Localization Nanoscopy Images publication-title: Sci. Rep. doi: 10.1038/s41598-018-35053-8 – volume: 9 start-page: 6734 year: 2019 ident: ref_27 article-title: Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network publication-title: Sci. Rep. doi: 10.1038/s41598-019-42516-z – ident: ref_25 doi: 10.1109/FABS52071.2021.9702655 – volume: 36 start-page: 139 year: 2003 ident: ref_29 article-title: PhysioNet: An NIH research resource for complex signals publication-title: J. Electrocardiol. doi: 10.1016/j.jelectrocard.2003.09.038 – ident: ref_36 – volume: 37 start-page: 2093 year: 2016 ident: ref_19 article-title: Sparse representation-based ECG signal enhancement and QRS detection publication-title: Physiol. Meas. doi: 10.1088/0967-3334/37/12/2093 – volume: 43 start-page: 50 year: 2016 ident: ref_33 article-title: Adaptive estimation of statistical moments of the responses of random systems publication-title: Probabilistic Eng. Mech. doi: 10.1016/j.probengmech.2015.10.005 – volume: 43 start-page: 561 year: 2005 ident: ref_3 article-title: Atrial cell action potential parameter fitting using genetic algorithms publication-title: Med. Biol. Eng. Comput. doi: 10.1007/BF02351029 – volume: 12 start-page: 39 year: 2011 ident: ref_35 article-title: Adaptive Subgradient Methods for Online Learning and Stochastic Optimization publication-title: J. Mach. Learn. Res.
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SubjectTerms	Algorithms Artificial neural networks Cardiovascular disease Coding Deep learning Diagnosis Electrocardiogram Electrocardiography Electrodes Heart Heart diseases Methods Neural networks Noise Noise reduction Scientific validity Signal processing Signal to noise ratio Wavelet transforms
Title	ECG Signal Denoising Method Based on Disentangled Autoencoder
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