Skip to main content
SpringerLink
Log in

ECG Compression

  • Chapter
  • First Online:
ECG Acquisition and Automated Remote Processing

  • 1378 Accesses

Abstract

Compression techniques for biomedical data have been an active area of research for the last 50 years or more.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Jalaleddine SMS, Hutchens CG, Strattan RD, Coberly WA. ECG data compression techniques: a unified approach. IEEE Trans Biomed Eng. 1990;37(4):329–43.

    Article  Google Scholar 

  2. Schafer RW, Rabiner LR. A digital signal processing approach to interpolation. IEEE Proc. 1973;61:692–702.

    Article  Google Scholar 

  3. Davisson LD. An approximate theory of prediction for data compression. IEEE Tran Inf Theor. 1967;13(2):274–8.

    Article  Google Scholar 

  4. Davisson LD. Data compression using straight line interpolation. IEEE Tran Inf Theor. 1968;14(3):390–4.

    Article  Google Scholar 

  5. Kortman CM. Data compression by redundancy reduction. IEEE J Mag. 1967;4(3):133–9.

    Google Scholar 

  6. Cox JR, Nolle FM, Fozzard HA, Oliver GC. AZTEC: a preprocessing program for real time ECG rhythm analysis. IEEE Trans Biomed Eng. 1968;15(2):128–9.

    Article  Google Scholar 

  7. Kumar V, Saxena SC, Giri VK, Singh D. Improved modified AZTEC technique for ECG data compression: effect of parabolic filter on reconstructed signal. Comput Electri Eng 2005;31(4–5):224–344.

    Google Scholar 

  8. Furht B, Perez A. An adaptive real-time ECG compression algorithm with variable threshold. IEEE Trans Biomed Eng. 1988;35(6):489–94.

    Article  Google Scholar 

  9. Mueller WC. Arrhythmia detection program for an ambulatory ECG monitor. Biomed Sci Instrum. 1978;14:81–5.

    Google Scholar 

  10. Abenstein JP, Tompkins WJ. New data-reduction algorithm for real-time ECG analysis. IEEE Trans Biomed Eng 1982;BME-29(1):43–48.

    Google Scholar 

  11. Tompkins WJ, Webster JG, editors. Design of microcomputer based medical instrumentation. NJ, Prentice-Hall: Englewood Cliffs; 1981.

    Google Scholar 

  12. Gardenhire LW. ECG compression for biomedical telemetry. In: Caseres CA editors. Biomedical telemetry. New York: Academic; 1965, Chapter 11.

    Google Scholar 

  13. Gardenhire LW. Redundancy reduction the key to adaptive telemetry. In: Proceedings 1964 National telemetry conference, 1964. pp. 1–16.

    Google Scholar 

  14. Wolf HK, Sherwood J, Rautaharju PM. Digital transmission of electrocardiograms—a new approach. In: Proceedings of 4th Canadian medicine and biology conference, 1972. pp. 39a–39b.

    Google Scholar 

  15. Stewart D, Dower GE, Suranyi O. An ECG compression code. Electrocardiology. 1973;6(2):175–6.

    Article  Google Scholar 

  16. Kumar V, Saxena SC, Giri VK. Direct data compression of ECG signal for telemedicine. Int J Syst Sci. 2006;37(1):45–63.

    Article  MATH  Google Scholar 

  17. Kulkarni PK, Kumar V, Verma HK. Direct data compression techniques for ECG signals: effect of sampling frequency on performance. Int J Syst Sci. 1997;28(3):217–28.

    Article  MATH  Google Scholar 

  18. Allen VA, Belina J. ECG data compression using the discrete cosine transform (DCT). In: Proceedings of computers in cardiology, 11–14 Nov, Durham, NC; 1992. pp. 687–190.

    Google Scholar 

  19. Cetin AE, Koymen H, Aydin MC. Multichannel ECG data compression by multirate signal processing and transform domain coding technique. IEEE Trans Biomed Eng. 1993;40(5):495–9.

    Article  Google Scholar 

  20. Ahmed N, Milne PJ, Harris SG. Electrocardiographic data compression via orthogonal transforms. IEEE Trans Biomed Eng 1975;BME-22(6):484–487.

    Google Scholar 

  21. Istepenian RS, Petrosian A. Optimal zonal wavelet-based ECG data compression for a mobile telecardiology system. IEEE Trans Inf Tech Biomed. 2000;4(3):200–11.

    Article  Google Scholar 

  22. Kim BS, Yoo SK, Lee MH. Wavelet-based low-delay ECG compression algorithm for continuous ECG transmission. IEEE Trans Inf Tech Biomed. 2006;10(1):77–83.

    Article  Google Scholar 

  23. Manikandan MS, Dandapat S. Wavelet threshold based ECG compression using USZZQ and Huffman coding of DSM. Biomed Signal Process Control. 2006;1(4):261–70.

    Article  Google Scholar 

  24. Chan HL, Siao YC, Chen SW, Yu SF. Wavelet based ECG compression by bit-field preserving and running length encoding. Comput Methods Prog Biomed 2008;90(1):1–8.

    Google Scholar 

  25. Giakoumakis EA, Papakonstantinou G. An ECG data reduction algorithm. Comput cardiology, Oct 1986, Boston, MA; 1986. pp. 675–677.

    Google Scholar 

  26. Imai H, Kiraura N, Yoshida Y. An efficient coding method for electrocardiography using spline function. Syst Comput Jpn. 1985;16(3):85–94.

    Article  Google Scholar 

  27. Karczewicz M, Gabbouj M. ECG data compression using spline approximation. Signal Process 1997;59(1):43–59.

    Google Scholar 

  28. Sufi F, Kalil I. Diagnosis of cardiovascular abnormalities from compressed ECG: a data mining based approach. IEEE Tran Biomed Eng. 2011;15(1):33–9.

    Google Scholar 

  29. Sufi F, Khalil I, Mahmood AN. A clustering based system for instant detection of cardiac abnormalities from compressed ECG. Expert Sys Appl. 2011;38(5):4705–4713.

    Google Scholar 

  30. Health Insurance Portability and Accountability Act 1996, 104th Congress, Public law 104–191, 1996.

    Google Scholar 

  31. Health Insurance Portability and Accountability Act 1996 (HI-PAA), Centres for Medicine and Medicaid Services, 1996.

    Google Scholar 

  32. Sufi F, Kalil I. Enforcing secured ECG transmission for realtime telemonitoring: a joint encoding, compression, encryption mechanism. Secur Comm Netw. 2008;1(5):389–405.

    Google Scholar 

  33. Zigel Y, Cohen A, Katz A. A diagnostic meaningful distortion measure for ECG compression. In: Proceedings of 19th convention of electrical and electronics engineers in Israel, Nov 5–6, 1996, Israel; 1996. pp. 117–120.

    Google Scholar 

  34. Zigel Y, Cohen A, Katz A. The weighted diagnostic distortion (WDD) measure for ECG signal compression. IEEE Tran Biomed Eng. 2000;47(11):1422–30.

    Article  Google Scholar 

  35. Manikandan MS, Dandapat S. ECG distortion measures and their effectiveness. In: Proceedings of 1st international conference on emerging trends in engineering and technology (ICETET ‘08), July 16–18, 2008, Nagpur, India; 2008. pp. 705–710.

    Google Scholar 

  36. Manikandan MS, Dandapat S. Multiscale entropy-based weighted distortion measure for ECG coding. IEEE Signal Process Lett. 2008;15:829–32.

    Article  Google Scholar 

  37. Mitra M, Bera JN, Gupta R. Electrocardiogram compression technique for global system of mobile- based offline telecardiology application for rural clinics in India. IET Sci Meas Technol. 2012;6(6):412–9. The contribution from Biomedical Signal acquisition and Processing research group at Department of Applied Physics, University of Calcutta, India.

    Article  Google Scholar 

  38. Gupta R, Mitra M. An ECG compression technique for telecardiology application. In: Annual IEEE India conference (INDICON 2011), Dec 16–18, 2011, Hyderabad, India; 2011. pp. 1–4.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Applied Physics, University of Calcutta, APC Road 92, Kolkata, West Bengal, 700009, India

    Rajarshi Gupta, Madhuchhanda Mitra & Jitendranath Bera

Authors
  1. Rajarshi Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Madhuchhanda Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jitendranath Bera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rajarshi Gupta .

Appendices

Appendix I

Generation of array c[] from array b[] using data grouping concept

Appendix II

The algorithm for magnitude and sign encoding is separately given as follows:

Appendix III

The algorithm steps for zero sequence extraction from encoded (compressed) ECG

Appendix IV

The algorithm steps for magnitude and sign decoding from encoded (compressed) ECG

Appendix V

Generation of original ECG sampled by de-normalization and successive addition:

Appendix VI

The algorithm steps for 8-bit ASCII to 7-bit ASCII characters.

Appendix VII

Algorithms steps for sending formatted short messages to the GSM module

Serial communication settings for packet delivery MOD9001 GSM modem:

Baud 9,600, data bits 8, parity bit none, stop bit 1

  1. 1.

    open the compressed data file

  2. 2.

    put the characters in array x

  3. 3.

    convert characters in x into 7-bit ASCII characters in put in array y.

  4. 4.

    number of SMS to be transmitted, m = (y/159) + 1;

  5. 5.

    get the destination phone number from GUI, and put in variable ph_no

  6. 6.

    form the data packet \( \left| {\text{ transmit header }} \right|{\text{ ph}}\_{\text{no }}\,\left| {{\text{ m}} + 1 { }} \,\right|{\text{ end flag }}| \)

  7. 7.

    deliver the packet to GSM modem

  8. 8.

    i = 1; j = 1; msg_sl = 1;

  9. 9.

    if y(i) = 0Dh and y(i + 1) = 0Ah go to step 13

  10. 10.

    byte_frame (j) = y(i)

  11. 11.

    j = j+1; i = i + 1;

  12. 12.

    go to step 15

  13. 13.

    byte_frame(j) = y(i) + 06; byte_frame(j) = y(i) + 06

  14. 14.

    j = j + 2; i = i+2;

  15. 15.

    if j < 159 go to step 9

  16. 16.

    form the data packet \( \{ \left| {\text{ transmit header }} \right|{\text{ ph}}\_{\text{no }},\left| {{\text{ byte}}\_{\text{frame }}} \right|{\text{ msg sl no}}.\,\left| {\text{ end flag }} \right|\} \) and deliver to GSM modem using serial port

  17. 17.

    msg_sl = msg_sl + 1;

  18. 18.

    if msg_sl = m go to step 20

  19. 19.

    go to step 9 for next packet formation

  20. 20.

    close the compressed file

  21. 21.

    stop

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer India

About this chapter

Cite this chapter

Gupta, R., Mitra, M., Bera, J. (2014). ECG Compression. In: ECG Acquisition and Automated Remote Processing. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1557-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-1557-8_5

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1556-1

  • Online ISBN: 978-81-322-1557-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation