Skip to main content
SpringerLink
Log in

A Wavelet Denoising and Teager Energy Operator-Based Method for Automatic QRS Complex Detection in ECG Signal

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

Abstract

The electrocardiogram is an important tool that is widely used for diagnosis of many cardiovascular diseases. In this context, QRS complex detection is a very crucial step in the ECG diagnosis system. The major aim of this work is to develop a novel method for QRS complex detection under various ECG signal morphologies as well as under different ECG recording conditions, including numerous noise sources and varying QRS waveforms. The proposed algorithm is based principally on the stationary wavelet transform (SWT) and Teager energy operator (TEO). In our scheme, SWT is first used for ECG signal preprocessing and QRS complex frequency content localization. Subsequently, a novel process for R peak detection based on TEO and a moving average (MA) filter is introduced. More precisely, SWT is coupled with TEO and the MA filter to construct a smoothed detection mask. Then, after the mask segmentation and adaptive thresholding steps, R peak times are identified using the maxima detected on the created mask and employing a reference ECG signal. At this stage, efficient decision rules are applied for reducing the number of false alarms. In the experiments, we validate the proposed method on the well-known annotated MIT-BIH arrhythmia database (MITDB). The experimental results show that the newly proposed algorithm provides satisfactory detection performances compared to the recent state-of-the-art methods, with an average sensitivity of 99.84%, average positive predictivity (P+) of \(99.87\%\), detection error rate of 0.30% and an overall detection accuracy of 99.70%. Also, the proposed method presents a low computational time complexity with an average processing time of 12 s on each ECG record from MITDB.

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
Fig. 13
Fig. 14

Similar content being viewed by others

Notes

  1. This fact makes a time varying mask segmentation process.

  2. Based on our a priori knowledge about MITDB, the minimum time interval between two consecutive correctly annotated beats is exactly equal to 250 ms. This constraint has a physiological point of view that two heartbeats cannot physiologically happen in less than 250 ms [38].

References

  1. P.S. Addison, Wavelet transforms and the ECG : a review. Phys. Meas. 5(26), 155–199 (2005). https://doi.org/10.1088/0967-3334/26/5/R01

    Article  Google Scholar 

  2. V. Afonso, W. Tompkins, T. Nquyen, S. Luo, ECG beat detection using filter banks. IEEE Trans. Biomed. Eng. 46(2), 192–201 (1999)

    Google Scholar 

  3. A. Antoniadis, Wavelet methods in statistics: Some recent developments and their applications. Stat. Surv. 1, 16–55 (2007)

    MathSciNet  MATH  Google Scholar 

  4. Association for the Advancement of Medical Instrumentation: Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. ANSI/AAMI EC57 (1998)

  5. S. Banerjee, R. Gupta, M. Mitra, Delineation of ECG characteristic features using multiresolution wavelet analysis method. Measurement 45(3), 474–487 (2012). https://doi.org/10.1016/j.measurement.2011.10.025

    Article  Google Scholar 

  6. S. Benitez, P. Gaydecki, A. Zaidi, A. Fitzpatrick, The use of the Hilbert transform in ECG signal analysis. Comput. Biol. Med. 31, 399–406 (2001)

    Google Scholar 

  7. 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). https://doi.org/10.1049/iet-spr.2013.0391

    Article  Google Scholar 

  8. E.L. Bouny, L.M. Khalil, A. Adib, ECG signal filtering based on CEEMDAN with hybrid interval thresholding and higher order statistics to select relevant modes. Multimed. Tools. Appl. 78, 13067–13089 (2019). https://doi.org/10.1007/s11042-018-6143-x

    Article  Google Scholar 

  9. R.J. Brychta, S. Tuntrakool, M. Appalsamy, N.R. Keller, D. Robertson, R.G. Shiavi, A. Diedrich, Wavelet methods for spike detection in mouse renal sympathetic nerve activity. IEEE Trans. Biomed. Eng. 54(1), 82–93 (2007)

    Google Scholar 

  10. S. Chen, H. Chen, H. Chan, A real-time QRS method based on moving-averaging incorporating with wavelet denoising. Comput. Methods Prog. Biomed. 82(3), 187–195 (2006)

    Google Scholar 

  11. J.H. Choi, H.K. Jung, T. Kim, A new action potential detector using the mteo and its effects on spike sorting systems at low signal-to-noise ratios. IEEE Trans. Biomed. Eng. 53(4), 738–746 (2006)

    Google Scholar 

  12. G.D. Clifford, F. Azuaje, P.E. McSharry, Advanced methods and tools for ECG data analysis, in Artech House Engineering in Medicine and Biology Series (2006)

  13. A.K. Dohare, V. Kumar, R. Kumar, An efficient new method for the detection of QRS in electrocardiogram. Comput. Electr. Eng. 40(5), 1717–1730 (2014). https://doi.org/10.1016/j.compeleceng.2013.11.004

    Article  Google Scholar 

  14. D. Donoho, De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41(3), 613–627 (1995)

    MathSciNet  MATH  Google Scholar 

  15. L. EL Bouny, M. Khalil, A. Adib, ECG noise reduction based on stationary wavelet transform and zero-crossings interval thresholding, in IEEE ICEIT International Conference, Rabat, Morocco, pp. 1–6 (2017)

  16. A. Erdamar, F. Duman, S. Yetkin, A wavelet and teager energy operator based method for automatic detection of K-Complex in sleep EEG. Expert Syst. Appl. 39, 1284–1290 (2012). https://doi.org/10.1016/j.eswa.2011.07.138

    Article  Google Scholar 

  17. M.M. Falco Strasser, A.M Zoubir, Motion artifact removal in ECG signals using Multi-Resolution thresholding, in 20th European Signal Processing Conference (EUSIPCO), pp. 899–903 (2012)

  18. S. Farashi, A multiresolution time-dependent entropy method for QRS complex detection. Biomed. Signal Process. Control 24, 63–71 (2016). https://doi.org/10.1016/j.bspc.2015.09.008

    Article  Google Scholar 

  19. Z.E. Hadj Slimane, A. Nait-Ali, QRS complex detection using Empirical Mode Decomposition. Digit. Signal. Process 20(4), 1221–1228 (2010)

    Google Scholar 

  20. Y.H. Hu, W. Tompkins, J. Urrusti, V. Afonso, Applications of artificial neural networks for ECG signal detection and classification. J. Electrocardiol. 26, 66–73 (1993)

    Google Scholar 

  21. M.K. Islam, A. Rastegarnia, A.T. Nguyen, Z. Yang, Artifact characterization and removal for in vivo neural recording. J. Neurosci. Methods 226, 110–123 (2014). https://doi.org/10.1016/j.jneumeth.2014.01.027

    Article  Google Scholar 

  22. F. Jabloun, A.E. Cetin, E. Erzin, Teager energy based feature parameters for speech recognition in car noise. IEEE Signal Process. Lett. 6, 259–261 (1999)

    Google Scholar 

  23. I. Johnstone, B. Silverman, Wavelet threshold estimators for data with correlated noise. J. R. Stat. Soc. Ser. B (Gen.) 59, 319–351 (1997)

    MathSciNet  MATH  Google Scholar 

  24. J. Kaiser, On a simple algorithm to calculate the energy’of a signal, in IEEE Intrnational Conference on Acoustics Speech, Signal Processing (ICASSP), pp. 381–384 (1990)

  25. A. Karimipour, M.R. Homaeinezhad, Real-time electrocardiogram P-QRST detection-delineation algorithm based on quality-supported analysis of characteristic templates. Comput. Biol. Med. 52, 153–165 (2014). https://doi.org/10.1016/j.compbiomed.2014.07.002

    Article  Google Scholar 

  26. B. Kohler, C. Hennig, R. Orglmeiste, The principles of software QRS detection, in IEEE Engineering in Medicine and Biology Society, pp. 42–57 (2002)

  27. C.A. Ledezma, M. Altuve, Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings. Med. Biol. Eng. Comput. 57, 1673–1681 (2019). https://doi.org/10.1007/s11517-019-01990-3

    Article  Google Scholar 

  28. C. Li, C. Zheng, C. Tai, Detection of ECG characteristic points by wavelet transforms. IEEE Trans. Biomed. Eng. 42(1), 21–28 (1995)

    Google Scholar 

  29. Y. Li, X. Tang, Z. Xu, H. Yan, A novel approach to phase space reconstruction of single lead ECG for QRS complex detection. Biomed. Signal Process. Control 39, 405–415 (2018). https://doi.org/10.1016/j.bspc.2017.06.007

    Article  Google Scholar 

  30. F. Lieb, H.G.S. Stark, C. Thielemann, A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data. J. Neural Eng. 14(3), 036013 (2017). https://doi.org/10.1088/1741-2552/aa654b

    Article  Google Scholar 

  31. C.C. Lin, H.Y. Chang, Y.H. Huang, C.Y. Yeh, A novel wavelet-based algorithm for detection of QRS complex. Appl. Sci. 9(10), 2142–2160 (2019). https://doi.org/10.3390/app9102142

    Article  Google Scholar 

  32. E.J.S. Luz, W.R. Schwartz, G. Camara-Chavez, D. Menotti, ECG-based heartbeat classification for arrhythmia detection: a survey. Comput. Methods Prog. Biomed. 127, 144–164 (2016). https://doi.org/10.1016/j.cmpb.2015.12.008

    Article  Google Scholar 

  33. B. Mali, S. Zulj, R. Magjarevic, D. Miklavcic, T. Jarm, Matlab-based tool for ECG and HRV analysis. Biomed. Signal Process. Control 10, 108–116 (2014). https://doi.org/10.1016/j.bspc.2014.01.011

    Article  Google Scholar 

  34. S. Mallat, A Wavelet Tour of Signal Processing, 3rd edn. (Academic Press, Cambridge, 2009)

    MATH  Google Scholar 

  35. M.S. Manikandan, K. Soman, A novel method for detecting R-peaks in electrocardiogram (ECG) signal. Biomed. Signal Process. Control 7, 118–128 (2012). https://doi.org/10.1016/j.bspc.2011.03.004

    Article  Google Scholar 

  36. R. Mark, G. Moody, MIT-BIH-Arrhythmia Database, http://www.physionet.org/physiobank/database/mitdb

  37. J. Martinez, R. Almeida, S. Olmos, A. Rocha, P. Laguna, A wavelet based ECG delineator: evaluation on standard database. IEEE Trans. Biomed. Eng. 51(4), 570–581 (2004)

    Google Scholar 

  38. M. Merah, T. Abdelmalik, B. Larbi, R-peaks detection based on stationary wavelet transform. Comput. Methods Prog. Biomed. 121, 149–160 (2015). https://doi.org/10.1016/j.cmpb.2015.06.003

    Article  Google Scholar 

  39. G. Nason, B. Silverman, The stationary wavelet transform and some statistical applications. Anestis. Antoniad. George. Oppenh. Edit. Lec. Note. Stat. Wav. Stat. pp. 281–299 (1995)

  40. C. Nayak, S.K. Saha, R. Kar, D. Mandal, Automated QRS complex detection using MFO-based DFOD. IET Signal Process. 12, 1172–1184 (2018). https://doi.org/10.1049/iet-spr.2018.5230

    Article  Google Scholar 

  41. C. Nayak, S.K. Saha, R. Kar, D. Mandal, An efficient QRS complex detection using optimally designed digital differentiator. Circuit Syst. Signal Process. 38, 716–749 (2018). https://doi.org/10.1007/s00034-018-0880-y

    Article  Google Scholar 

  42. C. Nayak, S.K. Saha, R. Kar, D. Mandal, An optimally designed digital differentiator based preprocessor for R-peak detection in electrocardiogram signal. Biomed. Signal Process. Control 49, 440–464 (2019). https://doi.org/10.1016/j.bspc.2018.09.005

    Article  Google Scholar 

  43. T. Nguyen, X. Qin, A. Dinh, F. Bui, Low resource complexity R-peak detection based on triangle template matching and moving average filter. Sensors 19(18), 1–17 (2019). https://doi.org/10.3390/s19183997

    Article  Google Scholar 

  44. X. Ning, I. Selesnick, ECG enhancement and QRS detection based on sparse derivatives. Biomed. Signal Process. Control 8(6), 713–723 (2013). https://doi.org/10.1016/j.bspc.2013.06.005

    Article  Google Scholar 

  45. S. Pal, M. Mitra, Empirical mode decomposition based ECG enhancement and QRS detection. Comput. Biol. Med. 42, 83–92 (2012)

    Google Scholar 

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

    Google Scholar 

  47. D. Pandit, L. Zhang, C. Liu, S. Chattopadhyay, N. Aslam, C.P. Lim, A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm. Comput. Methods Prog. Biomed. 144, 61–75 (2017). https://doi.org/10.1016/j.cmpb.2017.02.028

    Article  Google Scholar 

  48. H.J. Park, D.-U. Jeong, K.S. Park, Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method. IEEE Trans. Biomed. Eng. 49(12), 1526–1533 (2002)

    Google Scholar 

  49. P. Phukpattaranont, QRS detection algorithm based on the quadratic filter. Expert Syst. Appl. 42, 4867–4877 (2015)

    Google Scholar 

  50. G.D. Poian, C.J. Rozell, R. Bernardini, R. Rinaldo, G.D. Clifford, Matched filtering for heart rate estimation on compressive sensing ECG measurements. IEEE Trans. Biomed. Eng. 65, 1349–1358 (2017). https://doi.org/10.1109/TBME.2017.2752422

    Article  Google Scholar 

  51. R. Poli, S. Cagnoni, G. Valli, Genetic design of optimum linear and nonlinear QRS detectors. IEEE Trans. Biomed. Eng. 42, 1137–1141 (1995)

    Google Scholar 

  52. P. Sabherwal, M. Agrawa, L. Singh, Automatic Detection of the R Peaks in Single-Lead ECG Signal. Circuit Syst. Signal Process. 36, 4637–4652 (2017). https://doi.org/10.1007/s00034-017-0537-2

    Article  Google Scholar 

  53. S. Sahoo, B. Kanungo, S. Behera, S. Sabut, Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities. Measurement 24, 63–71 (2017). https://doi.org/10.1016/j.measurement.2017.05.022

    Article  Google Scholar 

  54. K.B. Selcan, K.U. Alper, S.G. Efnan, E. Semih, G. Serkan, M. Bilginer Gulmezoglu, A survey on ECG analysis. Biomed. Signal Process. Control 43, 216–235 (2018). https://doi.org/10.1016/j.bspc.2018.03.003

    Article  Google Scholar 

  55. T. Sharma, K.K. Sharma, QRS complex detection in ECG signals using locally adaptive weighted total variation denoising. Comput. Biol. Med. 87, 187–199 (2017)

    Google Scholar 

  56. A. Sharma, S. Patidar, U.R. Acharya, Accurate tunable-Q wavelet transform based method for QRS complex detection. Comput. Electr. Eng. 75, 101–111 (2019). https://doi.org/10.1016/j.compeleceng.2019.01.025

    Article  Google Scholar 

  57. N. Thakor, J. Webstor, W. Thompkins, Estimation of the QRS complex power spectra for design of a QRS filter. IEEE Trans. Biomed. Eng. 31(11), 702–706 (1984)

    Google Scholar 

  58. O. Yakut, B. Emine Dogru, An improved QRS complex detection method having low computational load. Biomed. Signal Process. Control 42, 230–241 (2018). https://doi.org/10.1016/j.bspc.2018.02.004

    Article  Google Scholar 

  59. S. Yazdani, S. Fallet, J.M. Vesin, A novel short-term event extraction algorithm for biomedical signals. IEEE Trans. Biomed. Eng. 65, 754–762 (2017). https://doi.org/10.1109/TBME.2017.2718179

    Article  Google Scholar 

  60. 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). https://doi.org/10.1016/j.bspc.2015.10.011

    Article  Google Scholar 

  61. H. Zhu, J. Dong, An R-peak detection method based on peaks of Shannon energy envelope. Biomed. Signal Process. Control 8, 466–474 (2013). https://doi.org/10.1016/j.bspc.2013.01.001

    Article  Google Scholar 

  62. Z. Zidelmal, A. Amirou, M. Adnane, A. Belouchrani, QRS complex detection using wavelet coefficients. Comput. Methods Prog. Biomed. 107(3), 490–496 (2012). https://doi.org/10.1016/j.cmpb.2011.12.004

    Article  Google Scholar 

  63. Z. Zidelmal, A. Amirou, M. Adnane, A. Belouchrani, QRS complex detection using S-Transform and Shannon Energy. Comput. Methods Prog. Biomed. (2014). https://doi.org/10.1016/j.cmpb.2014.04.008

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Editor-in-chief as well as the anonymous associate editor and reviewers for their valuable suggestions and comments, which helped to substantially improve the quality of this paper. We would like also to thank dear colleague Sara Sekkate for her contribution to the language revision of the manuscript. This research was supported by the Center for Scientific and Technical Research of Morocco (CNRST) (Grant No: 18UH2C2017).

Author information

Authors and Affiliations

  1. LIM@II-FSTM, B.P. 146, Mohammedia, 20650, Morocco

    Lahcen El Bouny, Mohammed Khalil & Abdellah Adib

Authors
  1. Lahcen El Bouny
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammed Khalil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdellah Adib
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lahcen El Bouny.

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

El Bouny, L., Khalil, M. & Adib, A. A Wavelet Denoising and Teager Energy Operator-Based Method for Automatic QRS Complex Detection in ECG Signal. Circuits Syst Signal Process 39, 4943–4979 (2020). https://doi.org/10.1007/s00034-020-01397-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-020-01397-8

Keywords

Search

Navigation