skip to main content
10.1145/3647649.3647718acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicigpConference Proceedingsconference-collections
research-article

Multiscale Convolution and Attention based Denoising Autoencoder for Motion Artifact Removal in ECG Signals

Published: 03 May 2024 Publication History

Abstract

Abstract: Motion interference is a major issue in the process of acquiring electrocardiogram (ECG) signals. It introduces noise and artifacts into the ECG signal, causing signal distortion and deformation, thus affecting the accuracy of subsequent analyses and diagnoses. This study introduces a deep learning-based model for motion artifact removal in ECG signals, namely MSCT, which combines Multi-Scale Convolution with Transformer encoder to extract the local and global features of ECG signals and motion artifacts. The comparative experiments are conducted on the MIT-BIH arrythmia database which is contaminated with the motion artifact from MIT-BIH noise stress test database. Our MSCT model shows significantly higher Cosine Similarity and output signal-to-noise ratio (SNR), and lower Root Mean Square Error (RMSE) and Multi-Scale Entropy Based Weighted Percentage Root-mean-square Difference (MSEWPRD), compared with that of the three previous methods on the motion artifact-corrupted data with various input SNR (-6 dB, 0 dB and 6 dB). Especially, when the input SNR is -6 dB, the proposed MSCT achieves the results 6.15±2.83%, 0.25±0.14 mV, 0.91±0.08, 9.20±3.74 dB for MSEWPRD, RMSE, Cosine Similarity and SNR, respectively. All the four metrics, including quantitative and qualitative metrics, have demonstrated the superiority and robust of the MSCT model proposed in this study. Furthermore, the time-domain and frequency-domain plots of a randomly selected denoising segment at -6 dB effectively showcase the superior performance of our denoising method.

References

[1]
H. Kim, "Motion artifact removal using cascade adaptive filtering for ambulatory ECG monitoring system," 2012 IEEE biomedical circuits and systems conference (BioCAS), pp. 160-163, 2012.
[2]
S. Tian, J. Han, J. Yang, L. Zhou, and X. Zeng, "Motion artifact removal based on ICA for ambulatory ECG monitoring," 2015 IEEE 11th International Conference on ASIC (ASICON), pp. 1-4, 2015.
[3]
M. Zubair, G. N. V. S. C. Mouli, and R. A. Shaik, "Removal of Motion Artifacts from ECG signals by Combination of Recurrent Neural Networks and Deep Neural Networks," 2020 2nd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE), pp. 1-7, 28-28 Nov. 2020.
[4]
C.-H. Goh, L. K. Tan, N. H. Lovell, S.-C. Ng, M. P. Tan, and E. Lim, "Robust PPG motion artifact detection using a 1-D convolution neural network," Computer Methods and Programs in Biomedicine, vol. 196, p. 105596, 2020/11/01/ 2020.
[5]
E. Brophy, B. Hennelly, M. D. Vos, G. Boylan, and T. Ward, "Improved Electrode Motion Artefact Denoising in ECG Using Convolutional Neural Networks and a Custom Loss Function," IEEE Access, vol. 10, pp. 54891-54898, 2022.
[6]
H. T. Chiang, Y. Y. Hsieh, S. W. Fu, K. H. Hung, Y. Tsao, and S. Y. Chien, "Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders," IEEE Access, vol. 7, pp. 60806-60813, 2019.
[7]
F. P. Romero, D. C. Pinol, and C. R. Vazquez-Seisdedos, "DeepFilter: An ECG baseline wander removal filter using deep learning techniques," Biomedical Signal Processing and Control, vol. 70, Sep 2021.
[8]
C. Szegedy, "Going Deeper with Convolutions," IEEE Conference on Computer Vision and Pattern Recognition (Cvpr), pp. 1-9, 2015.
[9]
A. L. Goldberger, "PhysioBank, PhysioToolkit, and PhysioNet - Components of a new research resource for complex physiologic signals," Circulation, vol. 101, no. 23, pp. E215-E220, Jun 13 2000.
[10]
G. B. Moody, R. G. J. I. e. i. m. Mark, and b. magazine, "The impact of the MIT-BIH arrhythmia database," IEEE Engineering in Medicine and Biology, vol. 20, no. 3, pp. 45-50, 2001.
[11]
M. W. Moody GB, Mark RG, " A noise stress test for arrhythmia detectors," Computers in Cardiology, vol. 11, p. 4, 1984.
[12]
T. Wang, M. Sun, and K. Hu, "Dilated deep residual network for image denoising," 2017 IEEE 29th international conference on tools with artificial intelligence (ICTAI), pp. 1272-1279, 2017.
[13]
A. Vaswani, "Attention is All you Need," Advances in neural information processing systems, vol. 30, 2017.
[14]
M. S. Manikandan and S. J. I. S. P. L. Dandapat, "Multiscale entropy-based weighted distortion measure for ECG coding," IEEE Signal Processing Letters, vol. 15, pp. 829-832, 2008.

Cited By

View all

Index Terms

  1. Multiscale Convolution and Attention based Denoising Autoencoder for Motion Artifact Removal in ECG Signals

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Other conferences
        ICIGP '24: Proceedings of the 2024 7th International Conference on Image and Graphics Processing
        January 2024
        480 pages
        ISBN:9798400716720
        DOI:10.1145/3647649
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 03 May 2024

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. Dilation convolution
        2. Motion artifact
        3. Multi-head self-attention
        4. Multiscale convolution
        5. Noise Reduction

        Qualifiers

        • Research-article
        • Research
        • Refereed limited

        Conference

        ICIGP 2024

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • 0
          Total Citations
        • 67
          Total Downloads
        • Downloads (Last 12 months)67
        • Downloads (Last 6 weeks)11
        Reflects downloads up to 30 Jan 2025

        Other Metrics

        Citations

        Cited By

        View all

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format.

        HTML Format

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media