Skip to main content
SpringerLink
Log in

A New Moving Horizon Estimation Based Real-Time Motion Artifact Removal from Wavelet Subbands of ECG Signal Using Particle Filter

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

Motion artifact (MA) contamination into Electrocardiogram (ECG) signal is common issue during real-time data collection procedure. The removal of MA from the ECG signal is essential, because it impedes clinical features of the ECG. In this work, wavelet transform based real-time MA removal from ECG signal over wavelet subbands, is proposed. Initially, after beat detection, principal component analysis was performed on successive clean beats, occurred prior to corrupted beats, to extract feature beat. Now, based on the correlation coefficients between various frequency subbands of the feature beat and corrupted beat, modification of corrupted subbands was performed using a new approach of particle filter, governed by sequential Monte Carlo algorithm. A new moving horizon estimation algorithm was also proposed to estimate the unknown parameters of the corrupted subbands. The proposed work was tested on MIT-BIH arrhythmia records by adding MA signal ‘em’, from MIT-BIH Noise Stress Test database, with various signal-to-noise ratio (SNR). The experiment resulted in an average SNR improvement and CC of 16.423 dB and 0.9742, respectively, with an input SNR of 0 dB. To achieve the best accuracy during wavelet decomposition, a multilayer perceptron neural network was used to select optimal wavelet type. A comparison to previously published works proved the superiority of the proposed work.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

Data Availability

Data can be made available on request. 

References

  1. Gupta, R., Mitra, M., & Bera, J. (2014). ECG Acquisition and Automated Remote Processing. New Delhi: Springer India. https://doi.org/10.1007/978-81-322-1557-8

  2. Lee, J., McManus, D., Merchant, S., & Chon, K. (2012). Automatic Motion and Noise Artifact Detection in Holter ECG Data Using Empirical Mode Decomposition and Statistical Approaches. IEEE Transactions on Biomedical Engineering, 59(6),1499–1506. https://doi.org/10.1109/TBME.2011.2175729

  3. Banerjee, S., & Singh, G. K. (2021). Monte Carlo Filter-Based Motion Artifact Removal From Electrocardiogram Signal for Real-Time Telecardiology System. IEEE Transactions on Instrumentation and Measurement, 70, 4006110. https://doi.org/10.1109/TIM.2021.3102737

    Article  Google Scholar 

  4. Oster, J., et al. (2015). Semisupervised ECG ventricular beat classification with novelty detection based on switching Kalman filters. IEEE Transactions on Biomedical Engineering, 62(9), 2125–2134. https://doi.org/10.1109/TBME.2015.2402236

  5. Beach, C., et al. (2021). Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering. TechRxiv. https://doi.org/10.36227/techrxiv.13623908.v1

  6. Dembrani, M., Khanchandani, K., & Zurani, A. (2019). Accurate Detection of ECG Signals in ECG Monitoring Systems by Eliminating the Motion Artifacts and Improving the Signal Quality Using SSG Filter with DBE. Journal of Circuits, Systems and Computers, 29(2), 2050024. https://doi.org/10.1142/S0218126620500243

  7. Xiong, F., & Chen, D. (2020). CEEMDAN-IMFx-PCA-CICA: An improved single-channel blind source separation in multimedia environment for motion artifact reduction in ambulatory ECG. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-020-00188-7

    Article  Google Scholar 

  8. Mithun, P., et al. (2011). A wavelet based technique for suppression of EMG noise and motion artifact in ambulatory ECG. In Proceeding in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 7087–7090. https://doi.org/10.1109/IEMBS.2011.6091791

  9. Hashim, F., et al. (2012). Wavelet based motion artifact removal for ECG signals. In Proceeding in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 339–342. https://doi.org/10.1109/IECBES.2012.6498019

  10. Lin, H., et al. (2014). Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals. IRBM, 35(6), 351–361. https://doi.org/10.1016/j.irbm.2014.10.004

    Article  MathSciNet  Google Scholar 

  11. Abbaspour, S., Gholamhosseini, H., & Linden, M. (2015). Evaluation of Wavelet Based Methods in Removing Motion Artifact from ECG Signal. In Proceeding in 16th Nordic-Baltic Conference on Biomedical Engineering, 1–4. https://doi.org/10.1007/978-3-319-12967-9

  12. Singh, O., & Sunkaria, R. (2016). ECG signal denoising via empirical wavelet transform. Australasian Physical & Engineering Sciences in Medicine, 40(1), 219–229. https://doi.org/10.1007/s13246-016-0510-6

  13. Nagai, S., Anzai, D., & Wang, J. (2017). Motion artefact removals for wearable ECG using stationary wavelet transform. Healthcare Technology Letters, 4(4), 138–141. https://doi.org/10.1049/htl.2016.0100

  14. Berwal, D., et al. (2019). Motion Artifact Removal in Ambulatory ECG Signal for Heart Rate Variability Analysis. IEEE Sensors Journal, 19(24),12432–12442. https://doi.org/10.1109/JSEN.2019.2939391

  15. Xie, X., et al. (2021). A multi-stage denoising framework for ambulatory ECG signal based on domain knowledge and motion artifact detection. Future Generation Computer Systems, 116, 103–116. https://doi.org/10.1016/j.future.2020.10.024

  16. Rijnbeek, P., Kors, J., & Witsenburg, M. (2001). Minimum bandwidth requirements for recording of paediatric electrocardiograms. Circulation, 104(25), 3087–3090. https://doi.org/10.1161/hc5001.101063

  17. Mason, J., Hancock, E., & Gettes, L. (2007). Recommendations for the standardization and interpretation of the electrocardiogram. Circulation, 4(3), 413–419. https://doi.org/10.1161/CIRCULATIONAHA.106.180200

  18. Pan, J., & Tompkins, W. (1985). A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, 32(3), 230–236. https://doi.org/10.1109/TBME.1985.325532

    Article  Google Scholar 

  19. Banerjee, S., & Singh, G. K. (2021). Deep neural network based missing data prediction of electrocardiogram signal using multiagent reinforcement learning. Biomedical Signal Processing and Control, 67, 102508. https://doi.org/10.1016/j.bspc.2021.102508

  20. Banerjee, S., Gupta, R., & Saha J. (2018). Compression of Multilead Electrocardiogram using Principal Component Analysis and Machine Learning Approach. In Proceeding in 2018 IEEE Applied Signal Processing Conference (ASPCON), 24–28. https://doi.org/10.1109/ASPCON.2018.8748572

  21. Banerjee, S., & Singh G. (2021). Quality aware compression of multilead electrocardiogram signal using 2-mode tucker decomposition and steganography. Biomedical Signal Processing and Control, 64, 102230. https://doi.org/10.1016/j.bspc.2020.102230

  22. Kitagawa G. (1996). Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models. Journal of Computational and Graphical Statistics, 5(1), 1–25. https://doi.org/10.2307/1390750

  23. Banerjee, S. (2019). A First Derivative Based R-Peak Detection and DWT Based Beat Delineation Approach of Single Lead Electrocardiogram Signal. In Proceeding in 2019 IEEE Region 10 Symposium (TENSYMP), 565–570. https://doi.org/10.1109/TENSYMP46218.2019.8971094

  24. Chang, R., et al. (2014). High-Precision Real-Time Premature Ventricular Contraction (PVC) Detection System Based on Wavelet Transform. Journal of Signal Processing Systems, 77, 289–296. https://doi.org/10.1007/s11265-013-0823-6

    Article  Google Scholar 

  25. Sun, L., et al. (2015). Parameter estimation for towed cable systems using moving horizon estimation. IEEE Transactions on Aerospace and Electronic Systems, 51(2), 1432–1446. https://doi.org/10.1109/TAES.2014.130642

  26. Allgöwer, F., et al. (1999). Nonlinear Predictive Control and Moving Horizon Estimation — An Introductory Overview. Springer. Advances in Control, 391–449. https://doi.org/10.1007/978-1-4471-0853-5_19

  27. Rawlings, J., & Bakshi, B. (2006). Particle filtering and moving horizon estimation. Computers & Chemical Engineering, 30(10), 1529–1541. https://doi.org/10.1016/j.compchemeng.2006.05.031

  28. Fotouhi-Ghazvini, F., et al. (2017) Mobile cardiac health-care monitoring and notification with real time tachycardia and bradycardia arrhythmia detection. Journal of Medical Signals and Sensors, 7(4), 193–202. https://doi.org/10.4103/jmss.jmss_17_17

  29. Banerjee, S., & Singh, G. K. (2021). Quality Guaranteed ECG Signal Compression Using Tunable-Q Wavelet Transform and Möbius Transform-Based AFD. IEEE Transactions on Instrumentation and Measurement, 70(4008211), 1–11. https://doi.org/10.1109/TIM.2021.3122119

  30. Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C. H., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C. -K. (2000) Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23):e215-e220. Circulation Electronic Pages. Retrieved date June 13, 2000, from http://circ.ahajournals.org/content/101/23/e215.full

  31. Banerjee, S., & Singh, G. K. (2021). A new approach of ECG steganography and prediction using deep learning. Biomedical Signal Processing and Control, 64, 102151. https://doi.org/10.1016/j.bspc.2020.102151

  32. Banerjee, S., & Singh, G. K. (2022). Agent-based beat-by-beat compression of 12-lead electrocardiogram signal using adaptive Fourier decomposition. Biomedical Signal Processing and Control, 75, 103628. https://doi.org/10.1016/j.bspc.2022.103628

  33. Rakshit, M., & Das, S. (2017). An efficient wavelet-based automated R-peaks detection method using Hilbert transform. Biocybernetics and Biomedical Engineering, 37(3), 566–577. https://doi.org/10.1016/j.bbe.2017.02.002

  34. Sevinç, E. (2021). An empowered AdaBoost algorithm implementation: A COVID-19 dataset study. Computers & Industrial Engineering, 165, 107912. https://doi.org/10.1016/j.cie.2021.107912

  35. Shrestha, D. L., & Solomatine, D. P. (2006). Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression. Neural Computation, 18(7), 1678–1710. https://doi.org/10.1162/neco.2006.18.7.1678

  36. Zhang, F., & Lian, Y. (2011). QRS Detection Based on Morphological Filter and Energy Envelope for Applications in Body Sensor Networks. Journal of Signal Processing Systems, 64, 187–194. https://doi.org/10.1007/s11265-009-0430-8

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Head of the Department of Electrical Engineering, of Indian Institute of Technology Roorkee, for providing the necessary equipment of Biomedical Research Laboratory, where the entire research work was carried out. The authors sincerely thank Dr. Subhasis Mahato of R. G. Kar Medical College and Hospital Kolkata, for clinical annotations of the ECG data records.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Institute of Engineering and Management (Newtown campus), University of Engineering and Management, New Town, India

    Soumyendu Banerjee

  2. Department of Electrical Engineering, Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India

    Girish Kumar Singh

Authors
  1. Soumyendu Banerjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Girish Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soumyendu Banerjee.

Ethics declarations

Competing Interest

The authors declare that this paper reflects the authors' own research, which has neither been submitted, published, nor accepted for publication, elsewhere. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Banerjee, S., Singh, G.K. A New Moving Horizon Estimation Based Real-Time Motion Artifact Removal from Wavelet Subbands of ECG Signal Using Particle Filter. J Sign Process Syst 95, 1021–1035 (2023). https://doi.org/10.1007/s11265-023-01887-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-023-01887-3

Keywords

Search

Navigation