Skip to main content
SpringerLink
Log in

Designing an Iterative Adaptive Arithmetic Coding-Based Lossless Bio-signal Compression for Online Patient Monitoring System (IAALBC)

  • Conference paper
  • First Online:
Frontiers of ICT in Healthcare

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 519))

Abstract

Various new generation research works on real-time healthcare monitoring system are performing on to fulfill the demands of medical signal compression. In this paper, we proposed a state-of-the-art technique to compress and transmit large number of bio-signals like EEG, ECG, etc., of multiple patients over a network. The proposed bio-signal lossless compression system formed on iterative adaptive arithmetic coding technique. In an online patient monitoring system, using the proposed technique, multiple signals from large number of patients are compressed and sent over the network to the control room by saving bandwidth. The technique is comprised of restructuring frequency and magnitude and phase components followed by adaptive arithmetic coding. Therefore, the proposed mechanism reduces the complexities and at the same time increase the compression performance compared to the existing encoders. Assessing the performance of the methodology and the quality of the signal, several parameters such as compression ratio, SNR, PSNR, and entropy are used to analyze the experimental data.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Tejedor J et al (2019) Search on speech from spoken queries: the Multi-domain International ALBAYZIN 2018 Query-by-Example Spoken Term Detection Evaluation. EURASIP J Audio Speech Music Process 2019. Article number: 13

    Google Scholar 

  2. Uthayakumar J et al A survey on data compression techniques: from the perspective of data quality, coding schemes, data type and applications. J King Saud Univ Comput Inform Sci. https://doi.org/10.1016/j.jksuci.2018.05.006

  3. Cooley JW, Lewis PAW, Welch PD (1969) The fast Fourier transform and its applications. IEEE Trans Educ 12(1):27–34. https://doi.org/10.1109/TE.1969.4320436

    Article  Google Scholar 

  4. Serov V Formulation of Fourier series. In: Fourier series, Fourier transform and their applications to mathematical physics, vol 197. ISSN 0066-5452. Springer International Publishing. https://doi.org/10.1007/978-3-319-65262-7

  5. Wong MW Discrete Fourier analysis. Birkhäuser Basel. eBook ISBN 978-3-0348-0116-4, https://doi.org/10.1007/978-3-0348-0116-4

  6. Brigham EO, Morrow RE (1967) The fast Fourier transform. IEEE Spectr 4(12):63–70. https://doi.org/10.1109/MSPEC.1967.5217220

    Article  Google Scholar 

  7. Bergland GD (1969) A guided tour of the fast Fourier transform. IEEE Spectr 6(7):41–52. https://doi.org/10.1109/MSPEC.1969.5213896

    Article  Google Scholar 

  8. Loan CV Computational frameworks for the fast Fourier transform. ISBN 978-0-89871-285-8, https://doi.org/10.1137/1.9781611970999

  9. Howard PG, Vitter JS (1994) Arithmetic coding for data compression. Proc IEEE 82(6):857–865. https://doi.org/10.1109/5.286189

    Article  Google Scholar 

  10. Howard PG, Vitter JS (1992) Practical implementations of arithmetic coding. In: Storer JA (ed) Image and text compression. The Kluwer international series in engineering and computer science (Communication and information theory), vol 176. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3596-6_4

  11. http://www.monkeysaudio.com/. Accessed on 15.08.2021 at 11 AM

  12. http://www.wavpack.com/. Accessed on 14.08.2021 at 9 AM

  13. https://xiph.org/flac/. Accessed: 19-09-2021

  14. Chou CH, Wu TL (2003) Embedding color watermarks in color images. EURASIP J Adv Sig Process 2003:548941. https://doi.org/10.1155/S1110865703211227

    Article  Google Scholar 

  15. Manju M, Abarna P, Akila U, Yamini S (2018) Peak signal to noise ratio and mean square error calculation for various images using the lossless image compression in CCSDS algorithm. Int J Pure Appl Math 119(12):14471–14477

    Google Scholar 

  16. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

    Article  MATH  Google Scholar 

  17. Li ZN, Drew MS, Liu J (2014) Internet multimedia content distribution. In: Fundamentals of multimedia. Texts in computer science. Springer, Cham. https://doi.org/10.1007/978-3-319-05290-8_16

  18. Kutter M, Petitcolas F (1999) A fair benchmark for image watermarking systems. Proc SPIE Int Soc Opt Eng 3657. https://doi.org/10.1117/12.344672

  19. https://data.mendeley.com/datasets/7dybx7wyfn/3. Accessed: 19-09-2021

  20. Arnold M (200) Audio watermarking: features, applications and algorithms. In: 2000 IEEE international conference on multimedia and expo. ICME2000. Proceedings. Latest advances in the fast-changing world of multimedia (Cat. No.00TH8532), vol 2, pp 1013–1016. https://doi.org/10.1109/ICME.2000.871531

  21. Kim H, Wen J, Villasenor JD (2007) Secure arithmetic coding. IEEE Trans Sig Process 55(5):2263–2272. https://doi.org/10.1109/TSP.2007.892710

    Article  MATH  Google Scholar 

  22. Huang S-J, Jou M-J (2004) Application of arithmetic coding for electric power disturbance data compression with wavelet packet enhancement. IEEE Trans Power Syst 19(3):1334–1341. https://doi.org/10.1109/TPWRS.2004.825899

    Article  Google Scholar 

  23. Rubin F (1979) Arithmetic stream coding using fixed precision registers. IEEE Trans Inf Theor 25(6):672–675. https://doi.org/10.1109/TIT.1979.1056107

    Article  MATH  Google Scholar 

  24. Mondal U, Debnath A (2021) Developing a dynamic cluster quantization based lossless audio compression (DCQLAC). Multimedia Tools Appl 80:1–24. https://doi.org/10.1007/s11042-020-09886-3

  25. Mondal U, Debnath A, Mandal J (2020) Deep learning-based lossless audio encoder (DLLAE). https://doi.org/10.1007/978-981-15-4288-6_6

  26. Chua E, Fang WC (2011) Mixed bio-signal lossless data compressor for portable brain-heart monitoring systems. IEEE Trans Consum Electron 57:267–273

    Article  Google Scholar 

  27. Sriraam N, Eswaran C (2008) An adaptive error modeling scheme for the lossless compression of EEG signals. IEEE Trans Inf Tech Biomed 12:587–594

    Article  Google Scholar 

  28. https://physionet.org/content/auditory-eeg/1.0.0/. Accessed: 19-09-2021

  29. https://physionet.org/content/emgdb/1.0.0/. Accessed: 19-09-2021

  30. Debnath A, Mondal U, Roy B, Panja N (2020) Achieving lossless audio encoder through integrated approaches of wavelet transform, quantization and Huffman encoding (LAEIWQH), pp 1–5. https://doi.org/10.1109/ICCSEA49143.2020.9132865

  31. https://physionet.org/content/mitdb/1.0.0/. Accessed: 19-09-2021

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Vidyasagar University, Midnapore, West Bengal, India

    Uttam Kr. Mondal, Asish Debnath & N. Tabassum

  2. Department of CSE, Kalyani University, Kalyani, West Bengal, India

    J. K. Mandal

Authors
  1. Uttam Kr. Mondal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Asish Debnath
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. N. Tabassum
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. K. Mandal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Uttam Kr. Mondal .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    Jyotsna Kumar Mandal

  2. Department of Computer Science and Engineering, School of Computational Science, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India

    Debashis De

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mondal, U.K., Debnath, A., Tabassum, N., Mandal, J.K. (2023). Designing an Iterative Adaptive Arithmetic Coding-Based Lossless Bio-signal Compression for Online Patient Monitoring System (IAALBC). In: Mandal, J.K., De, D. (eds) Frontiers of ICT in Healthcare . Lecture Notes in Networks and Systems, vol 519. Springer, Singapore. https://doi.org/10.1007/978-981-19-5191-6_53

Download citation

Publish with us

Policies and ethics

Search

Navigation