Matching Pursuit Decomposition on Electrocardiograms for Joint Compression and QRS Detection

Abstract

The electrocardiogram (ECG) is relevant for several medical purposes. In this work, a novel analysis-by-synthesis method to process ECG signals is presented. It is based on the matching pursuit algorithm, which is employed here to decompose the ECG in the time domain. The main features of the ECG are extracted through a dictionary of triangular functions, due to their good correlation with the typical electrocardiographic waveforms, especially the R wave. The individual elements of this signal representation can be further employed for different processing tasks, such as ECG compression and QRS detection. Compression is required to store and transmit signals in situations related to massive acquisitions, frequent monitoring, high-resolution data, real-time needs or narrow bandwidths. QRS detection is not only essential to study the heart rate variability, but also the basis of automatic systems for ECG applications such as heartbeat classification or anomaly identification. In this study, it is shown how to employ the proposed processing approach to perform ECG compression and beat detection jointly. The resulting algorithm is tested over the whole MIT-BIH Arrhythmia Database, with a wide variety of ECG records, yielding both high compression and efficient QRS detection.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. 1.

    ANSI/AAMI EC57: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms (1998)

  2. 2.

    ANSI/AAMI EC13: Cardiac Monitors, Heart Rate Meters, and Alarms (2002)

  3. 3.

    E. Alickovic, A. Subasi, Effect of multiscale PCA de-noising in ECG beat classification for diagnosis of cardiovascular diseases. Circuits Syst. Signal Process. 34(2), 513–533 (2015)

    Article  Google Scholar 

  4. 4.

    R. Benzid, A. Messaoudi, A. Boussaad, Constrained ECG compression algorithm using the block-based discrete cosine transform. Digit. Signal Process. 18(1), 56–64 (2008)

    Article  Google Scholar 

  5. 5.

    M. Blanco-Velasco, F. Cruz-Roldán, J.I. Godino-Llorente, K.E. Barner, Wavelet packets feasibility study for the design of an ECG compressor. IEEE Trans. Biomed. Eng. 54(4), 766–769 (2007)

    Article  Google Scholar 

  6. 6.

    M. Blanco-Velasco, F. Cruz-Roldán, F. López, A.M. Bravo, D. Martínez, A low computational complexity algorithm for ECG signal compression. Med. Eng. Phys. 26(7), 553–568 (2004)

    Article  Google Scholar 

  7. 7.

    T. Chaspari, A. Tsiartas, L.I. Stein, S.A. Cermak, S.S. Narayanan, Sparse representation of electrodermal activity with knowledge-driven dictionaries. IEEE Trans. Biomed. Eng. 62(3), 960–971 (2015)

    Article  Google Scholar 

  8. 8.

    J. Chen, J. Ma, Y. Zhang, X. Shi, ECG compression based on wavelet transform and Golomb coding. Electron. Lett. 42(6), 322–324 (2006)

    Article  Google Scholar 

  9. 9.

    G.D. Clifford, F. Azuaje, P.E. McSharry, Advanced Methods And Tools for ECG Data Analysis (Artech House, Inc., Norwood, 2006)

    Google Scholar 

  10. 10.

    F. Cruz-Roldán, P. Martín, J. Sáez-Landete, M. Blanco-Velasco, T. Saramaki, A fast windowing-based technique exploiting spline functions for designing modulated filter banks. IEEE Trans. Circuits Syst. I Regul. Pap. 56(1), 168–178 (2009)

    MathSciNet  Article  Google Scholar 

  11. 11.

    F. dos Santos Guimaraes, L. Lovisolo, M. Blanco-Velasco,F. Cruz-Roldán, On the compression of ECG records employing triangular elements and analysis-by-synthesis modeling. In: 2010 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 3084–3087 (2010)

  12. 12.

    M. Elgendi, Fast QRS detection with an optimized knowledge-based method: evaluation on 11 standard ECG databases. PLOS ONE 8(9), 1–18 (2013)

    Article  Google Scholar 

  13. 13.

    M. Elgendi, Less is more in biosignal analysis: compressed data could open the door to faster and better diagnosis. Diseases 6(1), 1–3 (2018)

    Google Scholar 

  14. 14.

    M. Elgendi, B. Eskofier, S. Dokos, D. Abbott, Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems. PLOS ONE 9(1), 1–18 (2014)

    Article  Google Scholar 

  15. 15.

    M. Elgendi, A. Mohamed, R. Ward, Efficient ECG compression and QRS detection for e-health applications. Sci. Rep. 7(1), 1–16 (2017)

    Article  Google Scholar 

  16. 16.

    K. Engan, S.O. Aase, J.H. Husøy, Multi-frame compression: theory and design. Signal Process. 80(10), 2121–2140 (2000)

    MATH  Article  Google Scholar 

  17. 17.

    E. Everss-Villalba, F.M. Melgarejo-Meseguer, M. Blanco-Velasco, F.J. Gimeno-Blanes, S. Sala-Pla, J.L. Rojo-Álvarez, A. García-Alberola, Noise maps for quantitative and clinical severity towards long-term ECG monitoring. Sensors 17(11), 1–23 (2017)

    Article  Google Scholar 

  18. 18.

    R. Gutiérrez-Rivas, J.J. García, W.P. Marnane, A. Hernández, Novel real-time low-complexity QRS complex detector based on adaptive thresholding. IEEE Sens. J. 15(10), 6036–6043 (2015)

    Article  Google Scholar 

  19. 19.

    C. Hernando-Ramiro, M. Blanco-Velasco, L. Lovisolo, F. Cruz-Roldán, Consistent quality control in ECG compression by means of direct metrics. Physiol. Meas. 36(9), 1981–1994 (2015)

    Article  Google Scholar 

  20. 20.

    L. Hongmin, H. Yigang, Y. Sun, Detection of cardiac signal characteristic point using log-domain wavelet transform circuits. Circuits Syst. Signal Process. 27(5), 683–698 (2008)

    MathSciNet  MATH  Article  Google Scholar 

  21. 21.

    S.M.S. Jalaleddine, C.G. Hutchens, R.D. Stranttan, W.A. Coberly, ECG data compression techniques. a unified approach. IEEE Trans. Biomed. Eng. 37(4), 329–343 (1990)

    Article  Google Scholar 

  22. 22.

    B.-U. Kohler, C. Hennig, R. Orglmeister, The principles of software QRS detection. IEEE Eng. Med. Biol. Mag. 21(1), 42–57 (2002)

    Article  Google Scholar 

  23. 23.

    D. Li, H. Zhang, M. Zhang, Wavelet de-noising and genetic algorithm-based least squares twin SVM for classification of arrhythmias. Circuits Syst. Signal Process. 36(7), 2828–2846 (2016)

    MathSciNet  Article  Google Scholar 

  24. 24.

    H. Li, H. Liang, C. Miao, L. Cao, X. Feng, C. Tang, E. Li, Novel ECG signal classification based on KICA nonlinear feature extraction. Circuits Syst. Signal Process. 35(4), 1187–1197 (2016)

    MathSciNet  Article  Google Scholar 

  25. 25.

    H. Li, X. Wang, L. Chen, E. Li, Denoising and R-peak detection of electrocardiogram signal based on EMD and improved approximate envelope. Circuits Syst. Signal Process. 33(4), 1261–1276 (2014)

    Article  Google Scholar 

  26. 26.

    L. Lovisolo, E.A. da Silva, P.S. Diniz, On the statistics of matching pursuit angles. Signal Process. 90(12), 3164–3184 (2010)

    MATH  Article  Google Scholar 

  27. 27.

    Y. Ma, T. Li, Y. Ma, K. Zhan, Novel real-time FPGA-based R-wave detection using lifting wavelet. Circuits Syst. Signal Process. 35(1), 281–299 (2016)

    MathSciNet  MATH  Article  Google Scholar 

  28. 28.

    S. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397–3415 (1993)

    MATH  Article  Google Scholar 

  29. 29.

    G.B. Moody, R.G. Mark, The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. 20(3), 45–50 (2001)

    Article  Google Scholar 

  30. 30.

    G.B. Moody, W.K. Muldrow, R.G. Mark, A noise stress test for arrhythmia detectors. In: 11th Computers in Cardiology Conference, pp. 381–384 (1984)

  31. 31.

    M. Nakashizuka, K. Niwa, H. Kikuchi, ECG data compression by matching pursuits with multiscale atoms. IEICE Trans. Fundam. E84–A(8), 1919–1932 (2001)

    Google Scholar 

  32. 32.

    A. Ouamri, A. Nait-Ali, ECG compression method using Lorentzian functions model. Dig. Signal Process. 17(1), 319–326 (2007)

    Article  Google Scholar 

  33. 33.

    J. Pan, W.J. Tompkins, A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME–32(3), 230–236 (1985)

    Article  Google Scholar 

  34. 34.

    A. Ravelomanantsoa, A. Rouane, H. Rabah, N. Ferveur, L. Collet, Design and implementation of a compressed sensing encoder: application to EMG and ECG wireless biosensors. Circuits Syst. Signal Process. 36(7), 2875–2892 (2017)

    Article  Google Scholar 

  35. 35.

    P. Sabherwal, M. Agrawal, L. Singh, Automatic detection of the R peaks in single-lead ECG signal. Circuits Syst. Signal Process. 36(11), 4637–4652 (2017)

    Article  Google Scholar 

  36. 36.

    K. Sayood, Introduction to Data Compression (Morgan Kaufmann Publishers, Burlington, 2000)

    Google Scholar 

  37. 37.

    S. Shamekhi, M.H. Sedaaghi, QRS detection based on matching pursuit algorithm. In: 17th Iranian Conference of Biomedical Engineering, ICBME 2010 (2010)

  38. 38.

    K. Skreeting, J.H. Husoy, S.O. Aase, Improved huffman coding using recursive splitting. In: Norwegian Signal Processing Symposium, NORSIG (1999)

  39. 39.

    K. Skretting, K. Engan, J.H. Husoy, ECG compression using signal dependent frames and matching pursuit. In: 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2005, pp. 585–588 (2005)

  40. 40.

    L. Sörnmo, P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications (Academic Press, London, 2005)

    Google Scholar 

  41. 41.

    Q. Tan, B. Fang, P. Wang, Improved simultaneous matching pursuit for multi-lead ECG data compression. In: 2010 International Conference on Measuring Technology and Mechatronics Automation, pp. 438–441 (2010)

  42. 42.

    M.P. Tcheou, L. Lovisolo, E.A. da Silva, M.A. Rodrigues, P.S. Diniz, Optimum rate-distortion dictionary selection for compression of atomic decompositions of electric disturbance signals. IEEE Signal Process. Lett. 14(2), 81–84 (2007)

    Article  Google Scholar 

  43. 43.

    Y. Wang, S. Doleschel, R. Wunderlich, S. Heinen, High energy efficient analog compressed sensing encoder for wireless ECG system. Microelectron. J. 56, 10–16 (2016)

    Article  Google Scholar 

  44. 44.

    M. Yaghoobi, L. Daudet, M.E. Davies, Parametric dictionary design for sparse coding. IEEE Trans. Signal Process. 57(12), 4800–4810 (2009)

    MathSciNet  MATH  Article  Google Scholar 

  45. 45.

    M. Yochum, C. Renaud, S. Jacquir, Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT. Biomed. Signal Process. Control 25, 46–52 (2016)

    Article  Google Scholar 

  46. 46.

    H. Zhang, C. Chen, Y. Wu, P. Li, 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 

  47. 47.

    Z. Zidelmal, A. Amirou, D. Ould-Abdeslam, A. Moukadem, A. Dieterlen, QRS detection using S-transform and Shannon energy. Comput. Methods Progr. Biomed. 116(1), 1–9 (2014)

    Article  Google Scholar 

  48. 48.

    Y. Zigel, A. Cohen, A. Katz, ECG signal compression using analysis by synthesis coding. IEEE Trans. Biomed. Eng. 47(10), 1308–1313 (2000)

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Carlos Hernando-Ramiro.

Additional information

This work has been partially supported by the Spanish Ministry of Economy and Competitiveness through Project TEC2015-64835-C3-1-R and by CNPq (Brazil) through Project 302829/2017-2.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hernando-Ramiro, C., Lovisolo, L., Cruz-Roldán, F. et al. Matching Pursuit Decomposition on Electrocardiograms for Joint Compression and QRS Detection. Circuits Syst Signal Process 38, 2653–2676 (2019). https://doi.org/10.1007/s00034-018-0986-2

Download citation

Keywords

  • ECG decomposition
  • ECG compression
  • QRS detection
  • Matching pursuit (MP)
  • Triangular atoms