Skip to main content
Springer Nature Link
Log in

Improved ECG signal compression quality using bat algorithm

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Recently, electrocardiogram (ECG) data has gained significant importance not only for the medical context but also for purposes related to the biometric context, particularly data security and privacy. In this article, we propose an efficient ECG compression method to ensure high reliability and efficiency in data retention and transmission with high quality and confidentiality to convince patient information safety. The proposed method presents two main contributions. First, the discrete wavelet transform (DWT) coefficients are optimally thresholded using bat algorithm (BA), which provides high efficiency in improving the data compression rate without degrading the quality of the reconstructed data. Second, we perfectly encode the quantized vector of the wavelet coefficients by a two-role encoder (TRE). The effectiveness of the proposed search is evaluated in terms of compression ratio (CR) and root mean square difference (PRD) on the MIT-BIH arrhythmia data set. The results of the simulation validate the effectiveness of the proposed method compared to state-of-the-art methods.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Abo-Zahhad M, Ahmed SM, Zakaria A (2012) An efficient technique for compressing ECG signals using QRS detection, estimation, and 2D DWT coefficients thresholding. Modelling and Simulation in Engineering, 2012

  2. Ahmeda SM, Abo-Zahhad M (2001) A new hybrid algorithm for ECG signal compression based on the wavelet transformation of the linearly predicted error. Med Eng Phys 23:117–126

    Article  Google Scholar 

  3. Alaa Maieed A, Anas Fouad A, Ahmed Haitham N (2020) Efficient and effective scheme for ECG compression, 2020 2nd annual international conference on information and sciences (AiCIS), 91-94, https://doi.org/10.1109/AiCIS51645.2020.00024

  4. Alzaq HY, Ustundag BB (2018) An optimized two-level discrete wavelet implementation using residue number system. EURASIP J Adv Signal Process 2018:1–16

    Article  Google Scholar 

  5. Benzid R, Marir F, Bouguechal N -E (2007) Electrocardiogram compression method based on the adaptive wavelet coefficients quantization combined to a modified two-role encoder. IEEE Signal Proc Lett 14:373–376

    Article  Google Scholar 

  6. Benzid R, Messaoudi A, Boussaad A (2008) Constrained ECG compression algorithm using the block-based discrete cosine transform. Digit Sig Process 18:56–64

    Article  Google Scholar 

  7. Birnbaum T, Ahar A, Blinder D, Schretter C, Kozacki T, Schelkens P (2019) Wave atoms for digital hologram compression. Appl Opt 58:6193–6203

    Article  Google Scholar 

  8. Blanco-Velasco M, Cruz-Roldan F, Godino-Llorente JI, Barner KE (2004) ECG Compression with retrieved quality guaranteed. Electron Lett 40:1466–1467

    Article  Google Scholar 

  9. Blanco-Velasco M, Fernando C-R, López-Ferreras F, Bravo-Santos A, Martinez-Munoz D (2004) A low computational complexity algorithm for ECG signal compression. Med Eng Phys 24:553–568

    Article  Google Scholar 

  10. Boucheham B (2007) ShaLTeRR: A contribution to short and long-term redundancy reduction in digital signals. Sig Process 87:2336–2347

    Article  MATH  Google Scholar 

  11. Boucheham B, Ferdi Y, Batouche MC (2006) Recursive versus sequential multiple error measures reduction: a curve simplification approach to ECG data compression. Comput Methods Programs Biomed 81:162–173

    Article  Google Scholar 

  12. Boukaache A, Doghmane N, Boudjehem D (2019) New SPIHT-based algorithm for electrocardiogram signal compression. J Mechan Med Biol 19:1950005

    Article  Google Scholar 

  13. Bousselmi S, Aloui N, Cherif A (2016) Adaptive speech compression based on discrete wave atoms transform. Int J Electric Comput Eng 6:2150

    Google Scholar 

  14. Butta S, Amandeep K, Jugraj S (2015) A review of ECG data compression techniques, International journal of computer applications, 116, Citeseer

  15. Chatterjee A, Nait-Ali A, Siarry P (2005) An input-delay neural-network-based approach for piecewise ECG signal compression. IEEE Trans Biomed Eng 52:945–947

    Article  Google Scholar 

  16. Das A, Nirmala SR (2017) Wavelet and PCA Based ECG Compression. Sig Process 2:2015

    Google Scholar 

  17. Devvrat T, Rajesh K, Ashish G (2021) Analysis of linear quantization based wavelet decomposition technique for electrocardiogram signal compression, 12, 118-130. https://doi.org/10.34218/IJEET.12.1.2021.013https://doi.org/10.34218/IJEET.12.1.2021.013

  18. Djohan A, Nguyen TQ, Tompkins WJ (1995) ECG Compression using discrete symmetric wavelet transform. Proceedings of 17th international conference of the engineering in medicine and biology society 1:167–168

    Google Scholar 

  19. Gao Y, Ku M, Qian T, Wang J (2017) FFT Formulations of adaptive Fourier decomposition. J Comput Appl Math 324:204–215

    Article  MathSciNet  MATH  Google Scholar 

  20. Ibaida A, Abuadbba A, Chilamkurti N (2021) Privacy-preserving compression model for efficient ioMT ECG sharing. Comput Commun 166:1–8

    Article  Google Scholar 

  21. Javaid R, Besar R, Abas FS (2008) Performance evaluation of percent root mean square difference for ecg signals compression. Signal Process An Int J (SPIJ) 48:1–9

    Google Scholar 

  22. Kumar R, Kumar A, Pandey RK (2013) Beta wavelet based ECG signal compression using lossless encoding with modified thresholding. Comput Electric Eng 39:130–140

    Article  Google Scholar 

  23. Kumar A, Ramana R, Samayveer S, Rama S, Manjeet K (2021) A robust digital ECG signal watermarking and compression using biorthogonal wavelet transform. Res Biomed Eng 37:79–85

    Article  Google Scholar 

  24. Leinonen M, Hewett RJ, Zhang X, Ying L, Demanet L (2013) High-dimensional wave atoms and compression of seismic datasets. 2013 SEG Annual Meeting OnePetro

  25. Lu Z, Kim DY, Pearlman WA (2000) Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm. IEEE Trans Biomed Eng 47:849–856

    Article  Google Scholar 

  26. Manikandan MS, Dandapat S (2006) Wavelet threshold based ECG compression using USZZQ and Huffman coding of DSM. Biomed Sig Process Control 4:261–270

    Article  Google Scholar 

  27. Nave G, Cohen A (1993) ECG Compression using long-term prediction. IEEE Trans Biomed Eng 40:877–885

    Article  Google Scholar 

  28. Ramakrishnan AG, Saha S (1997) ECG Coding by wavelet-based linear prediction. IEEE Trans Biomed Eng 44:1253–1261

    Article  Google Scholar 

  29. The MIT-BIH Arrhythmia Database (2005) http://www.physionet.org/physiobank/database/mitdb. Accessed: March 26, 2020

  30. Tun HM, Moe MK, Naing ZM (2017) Analysis on ECG data compression using wavelet transform technique. Int J Psychol Brain Sci 2:127–140

    Article  Google Scholar 

  31. Wang X, Meng J (2008) A 2-D ECG compression algorithm based on wavelet transform and vector quantization. Digit Sig Process 8:179–188

    Article  Google Scholar 

  32. Yang XS (2010) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010:65–74

    Article  MATH  Google Scholar 

  33. Yang XS (2012)

  34. Yang XS (2013) Bat algorithm: literature review and applications, arXiv:1308.3900

  35. Yang XS, He H (2016) Nature-inspired optimization algorithms in engineering: overview and applications, Nature-inspired computation in engineering. Springer, Berlin, pp 1–20

    Google Scholar 

  36. Zhao C, Chen Z, Meng J, Xiang X (2016) Electrocardiograph compression based on sifting process of empirical mode decomposition. Electron Lett 52:688–690

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Energy Systems Modeling Laboratory, University of Biskra, Biskra, Algeria

    Djamel Eddine Touil

  2. LESIA Laboratory of Research, Department of Electrical Engineering, University of Biskra, Biskra, Algeria

    Nadjiba Terki & Athmane Zitouni

Authors
  1. Djamel Eddine Touil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nadjiba Terki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Athmane Zitouni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Djamel Eddine Touil.

Ethics declarations

Conflict of Interests

We, the authors declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere. We further confirm the following:

  • There are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

  • All authors have participated in

    • conception and design, or analysis and interpretation of the data;

    • drafting the article or revising it critically for important intellectual content;

    • approval of the final version.

  • The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.

  • We understand that the Corresponding Author is the sole contact for the Editorial process. He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs.

  • We have provided a current, correct email address which is accessible by the Corresponding Author (tde.touil@gmail.com)

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Touil, D.E., Terki, N. & Zitouni, A. Improved ECG signal compression quality using bat algorithm. Multimed Tools Appl 82, 2749–2764 (2023). https://doi.org/10.1007/s11042-022-12881-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12881-5

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy