[go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Infimal convolution and AM-GM majorized total variation-based integrated approach for biosignal denoising

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Biomedical measurements are generally contaminated with substantial noise from various sources, including thermal noise, interference from other physiological signals, environment, or electrode movements. Complete restoration of biosignals from noisy measurements using linear filtering techniques is not feasible owing to the spectral overlapping problem. This paper proposes an arithmetic–geometric mean inequality-based robust denoising method. The proposed method incorporates a novel convexified cost function using the concept of majorization-minimization. A two-step algorithm is derived using the forward–backward splitting technique. An optimality condition is derived to set the hyperparameters of the new algorithm. The proposed convex optimization-based method effectively denoises cardiovascular signals, including both electrocardiogram and photoplethysmography. Furthermore, the efficacy of the proposed approach is verified over different datasets. The quantitative and qualitative results obtained using the proposed method demonstrate the superiority of the proposed method in biosignal denoising concerning state-of-the-art techniques.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Algorithm 1
Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability statement

The data that support the experimental evaluations in this study are collected from the online available MIT-BIH Arrhythmia, BIDMC PPG, and PhysioNet/CinC Challenge-2018 database, which are duly cited in this paper.

References

  1. Sadad, T., Bukhari, S.A.C., Munir, A., Ghani, A., El-Sherbeeny, A.M., Rauf, H.T.: Detection of cardiovascular disease based on ppg signals using machine learning with cloud computing. Comput. Intell. Neurosci. 2022 (2022)

  2. Tripathi, P.M., Kumar, A., Komaragiri, R., Kumar, M.: A review on computational methods for denoising and detecting ecg signals to detect cardiovascular diseases. Arch. Comput. Methods Eng., 1–40 (2021)

  3. Allen, J.: Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 28(3), 1 (2007)

    Article  Google Scholar 

  4. Muduli, P.R., Kumar, V.: A proximity operator-based method for denoising biomedical measurements. Circuits, Syst. Signal Process., 1–25 (2023)

  5. Singh, P., Pradhan, G., Shahnawazuddin, S.: Denoising of ecg signal by non-local estimation of approximation coefficients in DWT. Biocybern. Biomed. Eng. 37(3), 599–610 (2017)

    Article  Google Scholar 

  6. Kabir, M.A., Shahnaz, C.: Denoising of ecg signals based on noise reduction algorithms in emd and wavelet domains. Biomed. Signal Process. Control 7(5), 481–489 (2012)

    Article  Google Scholar 

  7. Madan, P., Singh, V., Singh, D.P., Diwakar, M., Kishor, A.: Denoising of ecg signals using weighted stationary wavelet total variation. Biomed. Signal Process. Control 73, 103478 (2022)

    Article  Google Scholar 

  8. Kumar, A., Tomar, H., Mehla, V.K., Komaragiri, R., Kumar, M.: Stationary wavelet transform based ecg signal denoising method. ISA Transact. 114, 251–262 (2021)

    Article  Google Scholar 

  9. Tracey, B.H., Miller, E.L.: Nonlocal means denoising of ecg signals. IEEE Transact. Biomed. Eng. 59(9), 2383–2386 (2012)

    Article  Google Scholar 

  10. Muduli, P.R., Mukherjee, A.: A moreau envelope-based nonlinear filtering approach to denoising physiological signals. IEEE Transact. Instrum. Meas. 69(4), 1041–1050 (2019)

    Article  ADS  Google Scholar 

  11. Antczak, K.: Deep recurrent neural networks for ecg signal denoising. arXiv preprint arXiv:1807.11551 (2018)

  12. Selvakumarasamy, K., Poornachandra, S., Amutha, R.: K-shrinkage function for ecg signal denoising. J. Med. Syst. 43, 1–9 (2019)

    Article  Google Scholar 

  13. Lahmiri, S., Boukadoum, M.: A weighted bio-signal denoising approach using empirical mode decomposition. Biomed. Eng. Lett. 5, 131–139 (2015)

    Article  Google Scholar 

  14. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transact. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)

    Article  ADS  Google Scholar 

  15. Condat, L.: A direct algorithm for 1-D total variation denoising. IEEE Signal Process. Lett. 20(11), 1054–1057 (2013)

    Article  ADS  Google Scholar 

  16. Donoho, D.L., Johnstone, J.M.: Ideal Spatial Adaptation by Wavelet Shrinkage. Biometrika 81(3), 425–455 (1994)

    Article  MathSciNet  Google Scholar 

  17. Nason, G.P., Silverman, B.W.: The stationary wavelet transform and some statistical applications. Wavelets Stat., 281–299 (1995)

  18. Sharma, T., Sharma, K.K.: Qrs complex detection in ecg signals using locally adaptive weighted total variation denoising. Comput. Biol. Med. 87, 187–199 (2017)

    Article  PubMed  Google Scholar 

  19. Fotiadou, E., Vullings, R.: Multi-channel fetal ecg denoising with deep convolutional neural networks. Front. Pediatrics 8, 508 (2020)

    Article  Google Scholar 

  20. Chiang, H.-T., Hsieh, Y.-Y., Fu, S.-W., Hung, K.-H., Tsao, Y., Chien, S.-Y.: Noise reduction in ecg signals using fully convolutional denoising autoencoders. IEEE Access 7, 60806–60813 (2019)

    Article  Google Scholar 

  21. Combettes, P.L., Wajs, V.R.: Signal recovery by proximal forward-backward splitting. Multiscale Modeling Simul. 4(4), 1168–1200 (2005)

    Article  MathSciNet  Google Scholar 

  22. Sun, Y., Babu, P., Palomar, D.P.: Majorization-minimization algorithms in signal processing, communications, and machine learning. IEEE Trans. Signal Process. 65(3), 794–816 (2017)

    Article  ADS  MathSciNet  Google Scholar 

  23. Bauschke, H.H., Combettes, P.L.: Convex Analysis and Monotone Operator Theory in Hilbert Spaces, vol. 2011. Springer, New York, NY, USA (2017)

    Book  Google Scholar 

  24. Selesnick, I.: Total variation denoising via the Moreau envelope. IEEE Signal Process. Lett. 24(2), 216–220 (2017)

    Article  ADS  MathSciNet  Google Scholar 

  25. Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)

    Article  CAS  PubMed  Google Scholar 

  26. Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101(23), 215–220 (2000)

    Article  Google Scholar 

  27. Ghassemi, M.M., Moody, B.E., Lehman, L.-W.H., Song, C., Li, Q., Sun, H., Mark, R.G., Westover, M.B., Clifford, G.D.: You snooze, you win: the physionet/computing in cardiology challenge 2018. In: 2018 Computing in Cardiology Conference (CinC), vol. 45, pp. 1–4 (2018). IEEE

  28. Pimentel, M.A., Johnson, A.E., Charlton, P.H., Birrenkott, D., Watkinson, P.J., Tarassenko, L., Clifton, D.A.: Toward a robust estimation of respiratory rate from pulse oximeters. IEEE Transact. Biomed. Eng. 64(8), 1914–1923 (2016)

    Article  Google Scholar 

Download references

Funding

The authors thank the Space Applications Centre (SAC), Indian Space Research Organization (ISRO), Department of Space, Govt. of India, for supporting this research work.

Author information

Authors and Affiliations

  1. Department of Electronics Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, India

    Vinit Kumar & Priya Ranjan Muduli

Authors
  1. Vinit Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Priya Ranjan Muduli
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

VK was involved in the methodology, experimentation, writing the original manuscript draft. PRM contributed to the conceptualization, reviewing and editing the manuscript, and overall supervision of this work.

Corresponding author

Correspondence to Priya Ranjan Muduli.

Ethics declarations

Conflict of interest

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.

Ethical approval

In this work, no human or animal was directly involved. The datasets used in this study are publicly available online. Hence, ethical approval was not required for this study.

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

Kumar, V., Muduli, P.R. Infimal convolution and AM-GM majorized total variation-based integrated approach for biosignal denoising. SIViP 18, 1919–1927 (2024). https://doi.org/10.1007/s11760-023-02902-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02902-7

Keywords

Search

Navigation