A novel recursive backtracking genetic programming-based algorithm for 12-lead ECG compression

  • Mohammad Feli
  • Fardin Abdali-MohammadiEmail author
Original Paper


ECG signal is among medical signals used to diagnose heart problems. A large volume of medical signal’s data in telemedicine systems causes problems in storing and sending tasks. In the present paper, a recursive algorithm with backtracking approach is used for ECG signal compression. This recursive algorithm constructs a mathematical estimator function for each segment of the signal using genetic programming algorithm. When all estimator functions of different segments of the signal are determined and put together, a piecewise-defined function is constructed. This function is utilized to generate a reconstructed signal in the receiver. The compression result is a set of compressed strings representing the piecewise-defined function which is coded through a text compression method. In order to improve the compression results in this method, the input signal is smoothed. MIT-BIH arrhythmia database is employed to test and evaluate the proposed algorithm. The results of this algorithm include the average of compression ratio that equals 30.97 and the percent root-mean-square difference that is equal to 2.38%, suggesting its better efficiency in comparison with other state-of-the-art methods.


Electrocardiograph Signal compression Genetic programming Backtracking algorithm 



  1. 1.
    Salomon, D.: Data Compression: The Complete Reference, vol. 1092. Springer, Berlin (2004)zbMATHGoogle Scholar
  2. 2.
    Abdali-Mohammadi, F., Sepahvand, M.: A deep learning based compression algorithm for 9DOF inertial measurement unit signals along with an error compensating mechanism. IEEE Sens. J. 19(2), 632–640 (2019)CrossRefGoogle Scholar
  3. 3.
    Manikandan, M.S., Dandapat, S.: Wavelet-based electrocardiogram signal compression methods and their performances: a prospective review. Biomed. Signal Process. Control. 14, 73–107 (2014)CrossRefGoogle Scholar
  4. 4.
    Kumar, V., Saxena, S.C., Giri, V.K., Singh, D.: Improved modified AZTEC technique for ECG data compression: effect of length of parabolic filter on reconstructed signal. Comput. Electr. Eng. 31(4–5), 334–344 (2005)CrossRefGoogle Scholar
  5. 5.
    Batista, L.V., Melcher, E.U.K., Carvalho, L.C.: Compression of ECG signals by optimized quantization of discrete cosine transform coefficients. Med. Eng. Phys. 23(2), 127–134 (2001)CrossRefGoogle Scholar
  6. 6.
    Lee, S., Kim, J., Lee, M.: A real-time ECG data compression and transmission algorithm for an e-health device. IEEE Trans. Biomed. Eng. 58(9), 2448–2455 (2011)CrossRefGoogle Scholar
  7. 7.
    Cetin, A.E., Koymen, H., Aydin, M.C.: Multichannel ECG data compression by multirate signal processing and transform domain coding techniques. IEEE Trans. Biomed. Eng. 40(5), 495–499 (1993)CrossRefGoogle Scholar
  8. 8.
    Kumar, R., Kumar, A., Singh, G.K.: Hybrid method based on singular value decomposition and embedded zero tree wavelet technique for ECG signal compression. Comput. Methods Prog. Biomed. 129, 135–148 (2016)CrossRefGoogle Scholar
  9. 9.
    Fathi, A., Faraji-kheirabadi, F.: ECG compression method based on adaptive quantization of main wavelet packet subbands. Signal Image Video Process. 10(8), 1433–1440 (2016)CrossRefGoogle Scholar
  10. 10.
    Ziran, P., Guojun, W., Jiang, H., Shuangwu, M.: Research and improvement of ECG compression algorithm based on EZW. Comput. Methods Prog. Biomed. 145, 157–166 (2017)CrossRefGoogle Scholar
  11. 11.
    Rajankar, S., Talbar, S.: A quality-on-demand electrocardiogram signal compression using modified set partitioning in hierarchical tree. Signal Image Video Process. 10(8), 1559–1566 (2016)CrossRefGoogle Scholar
  12. 12.
    Aydin, M.C., Cetin, A.E., Koymen, H.: ECG data compression by sub-band coding. Electron. Lett. 27(4), 359–360 (1991)CrossRefGoogle Scholar
  13. 13.
    Manikandan, M. S., Dandapat, S.: ECG signal compression using discrete sinc interpolation. In: Intelligent Sensing and Information Processing, pp. 14–19 (2005)Google Scholar
  14. 14.
    Tchiotsop, D., Wolf, D., Louis-Dorr, V., Husson, R.: ECG data compression using Jacobi polynomials. In: Engineering in Medicine and Biology Society, 29th Annual International Conference of the IEEE, pp. 1863–1867 (2007)Google Scholar
  15. 15.
    Ardhapurkar, S., Manthalkar, R., Gajre, S.: Electrocardiogram compression by linear prediction and wavelet sub-band coding techniques. Comput. Cardiol. 38, 141–144 (2011)Google Scholar
  16. 16.
    Zigel, Y., Cohen, A., Katz, A.: ECG signal compression using analysis by synthesis coding. IEEE Trans. Biomed. Eng. 47(10), 1308–1316 (2000)CrossRefGoogle Scholar
  17. 17.
    Miaou, S.G., Yen, H.L.: Multichannel ECG compression using multichannel adaptive vector quantization. IEEE Trans. Biomed. Eng. 48(10), 1203–1207 (2001)CrossRefGoogle Scholar
  18. 18.
    Sun, C.C., Tai, S.C.: Beat-based ECG compression using gain-shape vector quantization. IEEE Trans. Biomed. Eng. 52(11), 1882–1888 (2005)CrossRefGoogle Scholar
  19. 19.
    Chen, W.S., Hsieh, L., Yuan, S.Y.: High performance data compression method with pattern matching for biomedical ECG and arterial pulse waveforms. Comput. Methods Prog. Biomed. 74(1), 11–27 (2004)CrossRefGoogle Scholar
  20. 20.
    Chakraborty, M., Das, S.: Determination of signal to noise ratio of electrocardiograms filtered by band pass and Savitzky–Golay filters. Proc. Technol. 4, 830–833 (2012)CrossRefGoogle Scholar
  21. 21.
    Hargittai, S.: Savitzky–Golay least-squares polynomial filters in ECG signal processing. Comput. Cardiol. 32, 763–766 (2005)Google Scholar
  22. 22.
    Cetin, A. E., Tofighi, M.: Denosing using wavelets and projections onto the l1-ball. arXiv preprint arXiv. 1406.2528 (2014)
  23. 23.
    Du, H., Liu, Y.: Minmax-concave total variation denoising. Signal Image Video Process. 12, 1–8 (2018)CrossRefGoogle Scholar
  24. 24.
    Bassiouni, M.M., El-Dahshan, E.S.A., Khalefa, W., Salem, A.M.: Intelligent hybrid approaches for human ECG signals identification. Signal Image Video Process. 12(5), 941–949 (2018)CrossRefGoogle Scholar
  25. 25.
    Savitzky, A., Golay, M.J.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627–1639 (1964)CrossRefGoogle Scholar
  26. 26.
    Schafer, R.W.: What is a Savitzky–Golay filter? [lecture notes]. IEEE Signal Process. Mag. 28(4), 111–117 (2011)CrossRefGoogle Scholar
  27. 27.
    Acharya, D., Rani, A., Agarwal, S., Singh, V.: Application of adaptive Savitzky–Golay filter for EEG signal processing. Perspect. Sci. 8, 677–679 (2016)CrossRefGoogle Scholar
  28. 28.
    Martnez, A., Alcaraz, R., Rieta, J.J.: Application of the phasor transform for automatic delineation of single-lead ECG fiducial points. Physiol. Meas. 31(11), 1467 (2010)CrossRefGoogle Scholar
  29. 29.
    Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  30. 30.
    Sepahvand, M., Abdali-Mohammadi, F., Mardukhi, F.: Evolutionary metric-learning-based recognition algorithm for online isolated Persian/Arabic characters, reconstructed using inertial pen signals. IEEE Trans. Cybern. 47(9), 2872–2884 (2017)CrossRefGoogle Scholar
  31. 31.
    Welch, T.A.: Technique for high-performance data compression. Computer 6, 8–19 (1984)CrossRefGoogle Scholar
  32. 32.
    The MIT-BIH Arrhythmia Database: (2005). Accessed Jan 2018
  33. 33.
    Benzid, R., Marir, F., Bouguechal, N.E.: Electrocardiogram compression method based on the adaptive wavelet coefficients quantization combined to a modified two-role encoder. IEEE Signal Process. Lett. 14(6), 373–376 (2007)CrossRefGoogle Scholar
  34. 34.
    Agulhari, C.M., Bonatti, I.S., Peres, P.L.: An Adaptive Run Length Encoding method for the compression of electrocardiograms. Med. Eng. Phys. 35(2), 145–153 (2013)CrossRefGoogle Scholar
  35. 35.
    Zhang, H.X., Chen, C.F., Wu, Y.L., Li, P.H.: Decomposition and compression for ECG and EEG signals with sequence index coding method based on matching pursuit. J. China Univ. Posts Telecommun. 19(2), 92–95 (2012)CrossRefGoogle Scholar
  36. 36.
    Chou, H.H., Chen, Y.J., Shiau, Y.C., Kuo, T.S.: An effective and efficient compression algorithm for ECG signals with irregular periods. IEEE Trans. Biomed. Eng. 53(6), 1198–1205 (2006)CrossRefGoogle Scholar
  37. 37.
    Bera, P., Gupta, R.: Hybrid encoding algorithm for real time compressed electrocardiogram acquisition. Measurement 91, 651–660 (2016)CrossRefGoogle Scholar
  38. 38.
    Huang, B., Wang, Y., Chen, J.: ECG compression using the context modeling arithmetic coding with dynamic learning vector-scalar quantization. Biomed. Signal Process. Control 8(1), 59–65 (2013)CrossRefGoogle Scholar
  39. 39.
    Blanco-Velasco, M., Cruz-Roldan, F., Godino-Llorente, J.I., Barner, K.E.: ECG compression with retrieved quality guaranteed. Electron. Lett. 40(23), 1466–1467 (2004)CrossRefGoogle Scholar
  40. 40.
    Moazami-Goudarzi, M., Moradi, M.H.: Electrocardiogram signal compression using multiwavelet transform. Signal Process. 4, 12 (2005)Google Scholar
  41. 41.
    Eddie Filho, B.L., Rodrigues, N.M., da Silva, E.A., de Carvalho, M.B., de Faria, S.M., da Silva, V.M.: On ECG signal compression with 1-D multiscale recurrent patterns allied to preprocessing techniques. IEEE Trans. Biomed. Eng. 56(3), 896–900 (2009)CrossRefGoogle Scholar
  42. 42.
    Chen, J., Ma, J., Zhang, Y., Shi, X.: ECG compression based on wavelet transform and Golomb coding. Electron. Lett. 42(6), 322–324 (2006)CrossRefGoogle Scholar
  43. 43.
    Blanco-Velasco, M., Cruz-Roldan, F., Godino-Llorente, J.I., Barner, K.E.: Wavelet packets feasibility study for the design of an ECG compressor. IEEE Trans. Biomed. Eng. 54(4), 766–769 (2007)CrossRefGoogle Scholar
  44. 44.
    Aggarwal, V., Patterh, M.S.: Quality controlled ECG compression using essentially non-oscillatory point-value decomposition (ENOPV) technique. Digit. Signal Process. 22(6), 878–884 (2012)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering and Information TechnologyRazi UniversityKermanshahIran

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