research-article

ECG signal enhancement based on improved denoising auto-encoder

Authors:

Xiuling LiuAuthors Info & Claims

Engineering Applications of Artificial Intelligence, Volume 52, Issue C

Pages 194 - 202

https://doi.org/10.1016/j.engappai.2016.02.015

Published: 01 June 2016 Publication History

Abstract

The electrocardiogram (ECG) is a primary diagnostic tool for examining cardiac tissue and structures. ECG signals are often contaminated by noise, which can manifest with similar morphologies as an ECG waveform in the frequency domain. In this paper, a novel deep neural network (DNN) is proposed to solve the above mentioned problem. This DNN is created from an improved denoising auto-encoder (DAE) reformed by a wavelet transform (WT) method. A WT with scale-adaptive thresholding method is used to filter most of the noise. A DNN based on improved DAE is then used to remove any residual noise, which is often complex with an unknown distribution in the frequency domain. The proposed method was evaluated on ECG signals from the MIT-BIH Arrhythmia database, and added noise signals were obtained from the MIT-BIH Noise Stress Test database. The results show that the average of output signal-to-noise ratio (SNR) is from 21.56dB to 22.96dB, and the average of root mean square error (RMSE) is less than 0.037. The proposed method showed significant improvement in SNR and RMSE compared with the individual processing with either a WT or DAE, thus providing promising approaches for ECG signal enhancement.

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Cited By

Rezgui ANasraoui BTalbi M(2024)Electrocardiogram Denoising Based on SWT and WATV Using ANNsCircuits, Systems, and Signal Processing10.1007/s00034-023-02590-143:5(3004-3019)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s00034-023-02590-1
Wang BHettiarachchi CSuominen HDaskalaki E(2023)Comparative Assessment of Machine Learning Strategies for Electrocardiogram DenoisingAI 2023: Advances in Artificial Intelligence10.1007/978-981-99-8388-9_40(495-506)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1007/978-981-99-8388-9_40
Zhang YBanta AFu YJohn MPost ARazavi MCavallaro JAazhang BLin Y(2022)RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac ElectrogramsACM Journal on Emerging Technologies in Computing Systems10.1145/346537218:2(1-25)Online publication date: 16-Mar-2022
https://dl.acm.org/doi/10.1145/3465372
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cover image Engineering Applications of Artificial Intelligence

Engineering Applications of Artificial Intelligence Volume 52, Issue C

June 2016

248 pages

ISSN:0952-1976

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 June 2016

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Cited By

Rezgui ANasraoui BTalbi M(2024)Electrocardiogram Denoising Based on SWT and WATV Using ANNsCircuits, Systems, and Signal Processing10.1007/s00034-023-02590-143:5(3004-3019)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s00034-023-02590-1
Wang BHettiarachchi CSuominen HDaskalaki E(2023)Comparative Assessment of Machine Learning Strategies for Electrocardiogram DenoisingAI 2023: Advances in Artificial Intelligence10.1007/978-981-99-8388-9_40(495-506)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1007/978-981-99-8388-9_40
Zhang YBanta AFu YJohn MPost ARazavi MCavallaro JAazhang BLin Y(2022)RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac ElectrogramsACM Journal on Emerging Technologies in Computing Systems10.1145/346537218:2(1-25)Online publication date: 16-Mar-2022
https://dl.acm.org/doi/10.1145/3465372
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Assali INouira IAbidi ABedoui M(2022)Intelligent ECG Signal Filtering Method Based on SVM AlgorithmCircuits, Systems, and Signal Processing10.1007/s00034-022-02196-z42:3(1773-1791)Online publication date: 11-Oct-2022
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Arsene C(2020)Design of Deep Convolutional Neural Network Architectures for Denoising Electrocardiographic Signals2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)10.1109/CIBCB48159.2020.9277718(1-8)Online publication date: 27-Oct-2020
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