Skip to main content
SpringerLink
Log in

Novel Real-Time FPGA-Based R-Wave Detection Using Lifting Wavelet

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

The recently emerging portable devices have given rise to a pressing demand for developing the portable electrocardiogram monitoring device (P-ECG-MD), and a good P-ECG-MD requires that its detection algorithm should guarantee both the high accuracy and the low computational complexity. In this paper, a new real-time R-wave detection algorithm is proposed and implemented on the field programmable gate array (FPGA). The ECG signal is processed by lifting wavelet, and R-wave is detected by differential operations. Both the lifting wavelet transform and the differential operations have the low spatial and temporal computational complexity. We evaluate our algorithm on several manually annotated databases such as MIT–BIH Arrhythmia database, Supraventricular Arrhythmia database and MIT–BIH ST Change database. In order to demonstrate the effectiveness of the new algorithm, experiments are conducted using MATLAB and FPGA. MATLAB simulation results obtain a high accuracy rate of over 99.8 %. FPGA experimental results obtain an average recognition rate of 99.68 %, and the resource estimation report shows that our algorithm occupies few FPGA resources.

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
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. P. Albrecht, S-T segment characterization for long-term automated ECG analysis. M.S. thesis, MIT Department of Electrical Engineering and Computer Science, 1983

  2. F. Bouaziz, D. Boutana, M. Benidir, Automatic detection method of R-wave positions in electrocardiographic signals. The 24th international conference on microelectronics, Algiers, 1–4 (2012)

  3. F. Bouaziz, D. Boutana, M. Benidir, Multiresolution wavelet-based QRS complex detection algorithm suited to several abnormal morphologies. IET Signal Process. 8(7), 774–782 (2014)

    Article  Google Scholar 

  4. S.A. Chouakri, F. Bereksi-Reguig, A. Taleb-Ahmed, QRS complex detection based on multi wavelet packet decomposition. Appl. Math. Comput. 217, 9508–9525 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  5. I. Daubechies, W. Sweldens, Factoring wavelet transforms into lifting steps. J. Fourier Anal. Appl. 4, 247–268 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  6. J. Frank, S. Mannor, J. Pineau, D. Precup, Time series analysis using geometric template matching. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 740–754 (2013)

    Article  Google Scholar 

  7. A.L. Goldberger, L. Amaral, L. Glass, J.M. Hausdorff, P. Ch. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.-K. Peng, HE. Stanley, PhysioBank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220; [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]. Accessed 13 June 2000

  8. S.D. Greenwald, Improved detection and classification of arrhythmias in noise-corrupted electrocardiograms using contextual information. Ph.D. thesis, Harvard-MIT Division of Health Sciences and Technology (1990)

  9. MdA Kabir, C. Shahnaz, Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed. Signal Process. Control 7, 481–489 (2012)

    Article  Google Scholar 

  10. 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, 1261–1276 (2014)

    Article  Google Scholar 

  11. C. Li, C. Zheng, C. Tai, Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng. 42, 21–28 (1995)

    Article  Google Scholar 

  12. J.P.V. Madeiro, P.C. Cortez, J.A.L. Marques, C.R.V. Seisdedos, R.M.R.C. Sobrinho, An innovative approach of QRS segmentation based on first-derivative, Hilbert and Wavelet Transforms. Med. Eng. Phys. 34, 1236–1246 (2012)

    Article  Google Scholar 

  13. S.Z. Mahmoodabadi, A. Ahmadian, M.D. Abolhasani in ECG Feature Extraction Using Daubechies Wavelets. Proceedings of the Fifth IASTED International Conference on Visualization, Imaging and Image Processing, pp. 343–348 (2005)

  14. J.P. Martinez, R. Almeida, S. Olmos, A.P. Rocha, P. Laguna, A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans. Biomed. Eng. 51, 570–581 (2004)

    Article  Google Scholar 

  15. J. Mateo, A. Torres, M.-A. García, C. Sánchez, R. Cervigon, Robust Volterra filter design for enhancement of electroencephalogram signal processing. Circuits Syst. Signal Process. 32(1), 233–253 (2013)

    Article  Google Scholar 

  16. El.H.El. Mimouni, K. Mohammed, Novel real-time FPGA-based QRS detection using adaptive threshold with the previous smallest peak of ECG signal. J. Theor. Appl. Inf. Technol. 50(1), 33–43 (2013)

  17. 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 

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

    Article  Google Scholar 

  19. W. Sweldens, The lifting scheme: a construction of second generation of wavelets. SIAM J. Math. Anal. 29, 511–546 (1997)

    Article  MathSciNet  Google Scholar 

  20. N.V. Thakor, J.G. Webste, W.J. Tompkins, Estimation of QRS complex power spectra for design of a QRS filter. IEEE Trans. Biomed. Eng. BME–31, 702–705 (1984)

    Article  Google Scholar 

  21. A.M. Torres, J. Mateo, M.-A. García, J. L. Santos, Cancellation of powerline interference from biomedical signals using an improved affine projection algorithm. Circuits Syst. Signal Process. 1–16 (2014)

  22. E.D. Übeyli, Implementing wavelet transform/mixture of experts network for analysis of electrocardiogram beats. Expert Syst. 25(2), 150–162 (2008)

    Article  Google Scholar 

  23. W. Wang, X. Wang, T. Gao, S. Guo, Design of a sound localization system based on SOPC. IEEE Int. Conf. Signal Process. Commun. Comput. (ICSPCC) 310–314 (2012)

  24. Y. Zhang, Y. Tian, Z. Wang, Y. Ma, Y. Ma, An ECG intelligent monitoring system with MSP430 microcontroller. 8th international workshop on systems, signal processing and their applications (WoSSPA), 214–219 (2013)

  25. H. Zhu, J. Dong, An R-peak detection method based on peaks of Shannon energy envelope. Biomed. Signal Process. Control 8, 466–474 (2013)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Editor-in-Chief and the reviewers for their comments that have helped improve this paper. This work was supported in part by National Natural Science Foundation of China (No. 61175012 & 61201422), Natural Science Foundation of Gansu Province (No. 1208RJZA265) and Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20110211110026).

Conflict of interest

All authors do not have any financial and personal relationships with other people or organizations that could inappropriately influence (bias) our work.

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China

    Yurun Ma, Tongqing Li, Yide Ma & Kun Zhan

Authors
  1. Yurun Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tongqing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yide Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kun Zhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yide Ma.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, Y., Li, T., Ma, Y. et al. Novel Real-Time FPGA-Based R-Wave Detection Using Lifting Wavelet. Circuits Syst Signal Process 35, 281–299 (2016). https://doi.org/10.1007/s00034-015-0063-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-015-0063-z

Keywords

Search

Navigation