Journal of Medical Systems

, 41:166 | Cite as

Wavelet-based Encoding Scheme for Controlling Size of Compressed ECG Segments in Telecardiology Systems

  • Asiya M. Al-Busaidi
  • Lazhar Khriji
  • Farid Touati
  • Mohd Fadlee Rasid
  • Adel Ben Mnaouer
Mobile & Wireless Health
Part of the following topical collections:
  1. Mobile & Wireless Health


One of the major issues in time-critical medical applications using wireless technology is the size of the payload packet, which is generally designed to be very small to improve the transmission process. Using small packets to transmit continuous ECG data is still costly. Thus, data compression is commonly used to reduce the huge amount of ECG data transmitted through telecardiology devices. In this paper, a new ECG compression scheme is introduced to ensure that the compressed ECG segments fit into the available limited payload packets, while maintaining a fixed CR to preserve the diagnostic information. The scheme automatically divides the ECG block into segments, while maintaining other compression parameters fixed. This scheme adopts discrete wavelet transform (DWT) method to decompose the ECG data, bit-field preserving (BFP) method to preserve the quality of the DWT coefficients, and a modified running-length encoding (RLE) scheme to encode the coefficients. The proposed dynamic compression scheme showed promising results with a percentage packet reduction (PR) of about 85.39% at low percentage root-mean square difference (PRD) values, less than 1%. ECG records from MIT-BIH Arrhythmia Database were used to test the proposed method. The simulation results showed promising performance that satisfies the needs of portable telecardiology systems, like the limited payload size and low power consumption.


ECG Compression Discrete wavelet transform Running length encoding Payload packets 



Authors would like to express sincere appreciation to Qatar National Research Fund ”NPRP Grant #4-1207-2-474“. The in-kind support of Sultan Qaboos University is also acknowledged.

Compliance with Ethical Standards

Conflict of interests

The authors certify that they have NO conflict of interest in the subject matter or materials discussed in this manuscript.


  1. 1.
    Jalaleddine, S., Hutchens, C.G., Strattan, R.D., and Coberly, W.A., ECG Data compression techniques-a unified approach. IEEE Trans. Biomed. Eng. 37(4):329–343, 1990.CrossRefPubMedGoogle Scholar
  2. 2.
    Yazicioglu, R.F., Torfs, T. , Penders, J., Romero, I., Kim, H., Merken, P., Gyselinckx, B., Yoo, H.-J., and Van Hoof, C. : In: Engineering in Medicine and Biology Society, 2009. EMBC 2009 Annual International Conference of the IEEE, IEEE, Piscataway, pp 3205–3208, 2009Google Scholar
  3. 3.
    Marcelloni, F., and Vecchio, M., An efficient lossless compression algorithm for tiny nodes of monitoring wireless sensor networks. Comput. J. 52(8):969–987, 2009.CrossRefGoogle Scholar
  4. 4.
    Djohan, A., Nguyen, T.Q., and Tompkins, W.J.: In: Engineering in Medicine and Biology Society, 1995., IEEE 17th Annual Conference, vol 1, IEEE, Piscataway, pp 167–168 , 1995Google Scholar
  5. 5.
    Liu, J.-H., Hung, K.-C., and Wu, T.-C., Ecg compression using non-recursive wavelet transform with quality control. Int. J. Electron. 103(9):1550–1565, 2016.Google Scholar
  6. 6.
    Lu, Z., Kim, D.Y., and Pearlman, W.A., Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm. IEEE Trans. Biomed. Eng. 47(7):849–856, 2000.CrossRefPubMedGoogle Scholar
  7. 7.
    Chan, H.-L., Siao, Y.-C., Chen, S.-W., and Yu, S.-F., Wavelet-based ECG compression by bit-field preserving and running length encoding. Comput. Methods Prog. Biomed. 90(1):1–8, 2008.CrossRefGoogle Scholar
  8. 8.
    Manikandan, M.S., and Dandapat, S., Wavelet-based electrocardiogram signal compression methods and their performances: a prospective review. Biomed. Signal Process. Control 14:73–107, 2014.CrossRefGoogle Scholar
  9. 9.
    Jayachandran, E. S., P. J. K., and R. A. U., Analysis of myocardial infarction using discrete wavelet transform. J. Med. Syst. 34(6):985–992, 2010.
  10. 10.
    Bessmeltcev, V., and Katasonov, D., Application of discrete wavelet transform with changing presentation of coefficients in data compression in mobile ecg monitoring systems. Biomed. Eng. 50(1):35–39, 2016.CrossRefGoogle Scholar
  11. 11.
    Addison, P.S., The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. Boca Raton: CRC press, 2002.CrossRefGoogle Scholar
  12. 12.
    Rajankar, S., and Talbar, S., A quality-on-demand electrocardiogram signal compression using modified set partitioning in hierarchical tree. SIViP 10(8):1559–1566, 2016.CrossRefGoogle Scholar
  13. 13.
    Sahraeian, S., and Fatemizadeh, E.: Wavelet-based 2-D ECG data compression method using SPIHT and VQ coding. In: EUROCON The International Conference on Computer as a Tool, IEEE Piscataway, pp 133–137, 2007Google Scholar
  14. 14.
    Gurkan, H., Compression of ECG signals using variable-length classifıed vector sets and wavelet transforms. EURASIP Journal on Advances in Signal Processing 2012(1):1–17, 2012.CrossRefGoogle Scholar
  15. 15.
    Allen, V.A., and Belina, J.: ECG Data compression using the discrete cosine transform (DCT). In: Proceedings of Computers in Cardiology 1992, IEEE, Piscataway, pp 687–690, 1992Google Scholar
  16. 16.
    Cho, G.-Y., Lee, S.-J., and Lee, T.-R., An optimized compression algorithm for real-time ecg data transmission in wireless network of medical information systems. J. Med. Syst. 39(1):1–8, 2015.CrossRefGoogle Scholar
  17. 17.
    Lee, H.-W., Hung, K.-C., Wu, T.-C., and Ku, C.-T., A modified run-length coding towards the realization of a RRO-NRDPWT-based ecg data compression system. EURASIP Journal on Advances in Signal Processing 2011 (1):1–8, 2011.CrossRefGoogle Scholar
  18. 18.
    Liang, X., and Balasingham, I.: Performance analysis of the ieee 802.15. 4 based ECG monitoring network. In: Proceedings of the 7th IASTED International Conferences on Wireless and Optical Communications, pp. 99–104, 2007Google Scholar
  19. 19.
    Benzid, R., Messaoudi, A., and Boussaad, A., Constrained ecg compression algorithm using the block-based discrete cosine transform. Digital Signal Process. 18(1):56–64, 2008.CrossRefGoogle Scholar
  20. 20.
    Blanchett, T., Kember, G., and Fenton, G., KLT-Based quality controlled compression of single-lead ecg. IEEE Trans. Biomed. Eng. 45(7):942–945, 1998.CrossRefPubMedGoogle Scholar
  21. 21.
    Ahmed, S.M., Al-Shrouf, A., and Abo-Zahhad, M., ECG Data compression using optimal non-orthogonal wavelet transform. Med. Eng. Phys. 22(1):39–46, 2000.CrossRefPubMedGoogle Scholar
  22. 22.
    Mallat, S.G., A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7):674–693, 1989.CrossRefGoogle Scholar
  23. 23.
    Abbate, A., DeCusatis, C., and Das, P.K., Wavelets and subbands: fundamentals and applications. 1st edn. Berlin: Springer Science & Business Media, 2002.CrossRefGoogle Scholar
  24. 24.
    Croisier, A., Esteban, D., and Galand, C.: Perfect channel splitting by use of interpolation/decimation/tree decomposition techniques. In: International Conference on Information Sciences and Systems, vol 2, Patras, Greece, pp 443–446, 1976Google Scholar
  25. 25.
    Vaidyanathan, P.P., Multirate digital filters, filter banks, polyphase networks, and applications: a tutorial. Proc. IEEE 78(1):56–93, 1990.CrossRefGoogle Scholar
  26. 26.
    Vaidyanathan, P.P., Multirate systems and filter banks. India: Pearson Education, 1993.Google Scholar
  27. 27.
    Daubechies, I., Orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math. 41:909–996, 1988.CrossRefGoogle Scholar
  28. 28.
    Merah, M., Abdelmalik, T., and Larbi, B., R-peaks detection based on stationary wavelet transform. Comput. Methods Prog. Biomed. 121(3):149–160, 2015.CrossRefGoogle Scholar
  29. 29.
    Boutaa, M., Bereksi-Reguig, F., and Debbal, S., ECG Signal processing using multiresolution analysis. J. Med. Eng. Technol. 32(6):466–478, 2008.CrossRefPubMedGoogle Scholar
  30. 30.
    Abo-Zahhad, M., Al-Ajlouni, A.F., Ahmed, S.M., and Schilling, R.J., A new algorithm for the compression of ECG signals based on mother wavelet parameterization and best-threshold levels selection. Digital Signal Process. 23(3):1002–1011, 2013.CrossRefGoogle Scholar
  31. 31.
    Poornachandra, S., Wavelet-based denoising using subband dependent threshold for ECG signals. Digital signal process. 18(1):49–55, 2008.CrossRefGoogle Scholar
  32. 32.
    Donoho, D.L. , and Johnstone, I.M. : Threshold selection for wavelet shrinkage of noisy data. In: Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the 16th Annual International Conference of the IEEE Piscataway, pp. A24–A25, 1994Google Scholar
  33. 33.
    Quotb, A., Bornat, Y., and Renaud, S., Wavelet transform for real-time detection of action potentials in neural signals. Frontiers in Neuroengineering 4(7):10, 2011.Google Scholar
  34. 34.
    Alesanco, A., Istepanian, R., and Garcia, J.: The effects of transmission errors in ECG real-timewavelet compression codecs, In: Computers in Cardiology, 2005, IEEE, Piscataway, pp 45–48 , 2005Google Scholar
  35. 35.
    Déprez, F., Rioul, O., and Duhamel, P.: Border recovery for subband processing of finite-length signals. application to time-varying filter banks. In: 1994 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1994. ICASSP-94, vol 3, IEEE, Piscataway, pp III133, 1994Google Scholar
  36. 36.
    Jensen, A., and la Cour-Harbo, A., Ripples in mathematics: the discrete wavelet transform. Berlin: Springer Science & Business Media, 2001.CrossRefGoogle Scholar
  37. 37.
    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., and Stanley, H.E., Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220, 2000.CrossRefPubMedGoogle Scholar
  38. 38.
    Blanco-Velasco, M., Cruz-Roldán, F., Godino-Llorente, J.I., Blanco-Velasco, J., Armiens-Aparicio, C., and López-Ferreras, F., On the use of prd and cr parameters for ecg compression. Med. Eng. Phys. 27(9): 798–802, 2005.CrossRefPubMedGoogle Scholar
  39. 39.
    Fira, C.M., and Goras, L., An ECG signals compression method and its validation using NNs. IEEE Trans. Biomed. Eng. 55(4):1319–1326, 2008.CrossRefPubMedGoogle Scholar
  40. 40.
    Hilton, M.L., Wavelet and wavelet packet compression of electrocardiograms. IEEE Trans. Biomed. Eng. 44 (5):394–402, 1997.CrossRefPubMedGoogle Scholar
  41. 41.
    Al-Shrouf, A., Abo-Zahhad, M., and Ahmed, S.M., A novel compression algorithm for electrocardiogram signals based on the linear prediction of the wavelet coefficients. Digital Signal Process. 13(4):604–622, 2003.CrossRefGoogle Scholar
  42. 42.
    Touati, F., Erdene-Ochir, O., Mehmood, W., Hassan, A., Mnaouer, A.B., Gaabab, B., Rasid, M.F.A., and Khriji, L.: An experimental performance evaluation and compatibility study of the bluetooth low energy based platform for ECG monitoring in wbans. Int. J. Distrib. Sens. Netw. 2015:9, 2015Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer Engineering, College of EngineeringSultan Qaboos UniversityMuscatOman
  2. 2.Department of Electrical EngineeringQatar UniversityDohaQatar
  3. 3.Department of Computer and Communication Systems Engineering, Wireless and Photonics Network Research CenterUniversity of Putra MalaysiaSelangorMalaysia
  4. 4.School of Engineering, Applied Science and TechnologyCanadian University of DubaiDubaiUnited Arab Emirates

Personalised recommendations