Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

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.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Salomon, D.: Data Compression: The Complete Reference, vol. 1092. Springer, Berlin (2004)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  12. Aydin, M.C., Cetin, A.E., Koymen, H.: ECG data compression by sub-band coding. Electron. Lett. 27(4), 359–360 (1991)

    Article  Google Scholar 

  13. Manikandan, M. S., Dandapat, S.: ECG signal compression using discrete sinc interpolation. In: Intelligent Sensing and Information Processing, pp. 14–19 (2005)

  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)

  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. Zigel, Y., Cohen, A., Katz, A.: ECG signal compression using analysis by synthesis coding. IEEE Trans. Biomed. Eng. 47(10), 1308–1316 (2000)

    Article  Google Scholar 

  17. Miaou, S.G., Yen, H.L.: Multichannel ECG compression using multichannel adaptive vector quantization. IEEE Trans. Biomed. Eng. 48(10), 1203–1207 (2001)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  21. Hargittai, S.: Savitzky–Golay least-squares polynomial filters in ECG signal processing. Comput. Cardiol. 32, 763–766 (2005)

    Google Scholar 

  22. Cetin, A. E., Tofighi, M.: Denosing using wavelets and projections onto the l1-ball. arXiv preprint arXiv. 1406.2528 (2014)

  23. Du, H., Liu, Y.: Minmax-concave total variation denoising. Signal Image Video Process. 12, 1–8 (2018)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  25. Savitzky, A., Golay, M.J.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627–1639 (1964)

    Article  Google Scholar 

  26. Schafer, R.W.: What is a Savitzky–Golay filter? [lecture notes]. IEEE Signal Process. Mag. 28(4), 111–117 (2011)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  29. Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  31. Welch, T.A.: Technique for high-performance data compression. Computer 6, 8–19 (1984)

    Article  Google Scholar 

  32. The MIT-BIH Arrhythmia Database: https://www.physionet.org/physiobank/database/mitdb (2005). Accessed Jan 2018

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  37. Bera, P., Gupta, R.: Hybrid encoding algorithm for real time compressed electrocardiogram acquisition. Measurement 91, 651–660 (2016)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  40. Moazami-Goudarzi, M., Moradi, M.H.: Electrocardiogram signal compression using multiwavelet transform. Signal Process. 4, 12 (2005)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran

    Mohammad Feli & Fardin Abdali-Mohammadi

Authors
  1. Mohammad Feli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fardin Abdali-Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fardin Abdali-Mohammadi.

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

Feli, M., Abdali-Mohammadi, F. A novel recursive backtracking genetic programming-based algorithm for 12-lead ECG compression. SIViP 13, 1029–1036 (2019). https://doi.org/10.1007/s11760-019-01441-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01441-4

Keywords

Search

Navigation