Skip to main content
SpringerLink
Log in

Efficient R-peak Detection in Electrocardiogram Signal Based on Features Extracted Using Hilbert Transform and Burg Method

  • Original Contribution
  • Published:
Journal of The Institution of Engineers (India): Series B Aims and scope Submit manuscript

Abstract

Electrocardiogram (ECG) is a non-invasive test which is highly adopted as a primary diagnostic tool for cardiovascular diseases. ECG recording appears as a non-stationary and quasi-periodic electrical signal. This electrical signal has important segments: P-wave, QRS-complex, and T-wave. R-peak is an important component of these segments. Computer-aided diagnosis is preferable as manual diagnosis using naked eye may mislead the detection. Therefore, in this paper, features extracted using both Burg method of autoregressive (AR) modeling and Hilbert transform are used for enabling efficient automated R-peak detection in ECG signal. Burg method is considered for extracting features due to its better frequency resolution, flexibility in selecting AR model orders and faster convergence for short-time signals. Next, Hilbert transform is used to find missed information in terms of the spectral components. The proposed technique is validated using Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia database. K-nearest neighbor (KNN) classifier is used for classification as it requires only few parameters to be tuned (K and the distance metric). In this paper K = 3 is selected to avoid any tie situation and Euclidean distance metric is selected because it does not require any weights for features. Also, KNN is more effective for classifying three classes as compared to those handled by other existing classifiers. The performance of the proposed technique is evaluated on the basis of sensitivity (Se), positive predictivity (PP), accuracy (Acc) and duplicity (D). The proposed work yielded Se of 99.90%, PP of 99.93%, Acc of 99.84%, and D of 0.006361%. These results indicate improvement in heart diagnostic leading to correct treatment of the subject (patient) over other existing 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
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. B. Vishwanath, R.V. Pujeri, G. Devanagavi, Probabilistic principal component analysis-based dimensionality reduction and optimization for arrhythmia classification using ECG signals. Bio-Algorithms Med-Syst. 15(1), 1–15 (2019)

    Google Scholar 

  2. S.S. Mehta, N.S. Lingayat, SVM-based algorithm for recognition of QRS complexes in electrocardiogram. IRBM 29, 310–317 (2008)

    Article  Google Scholar 

  3. Z. Wang, F. Wan, C.M. Wong, L. Zhang, Adaptive Fourier decomposition based ECG denoising. Comput. Biol. Med. 77, 195–205 (2016)

    Article  Google Scholar 

  4. S. Jain, M.K. Ahirwal, A. Kumar, V. Bajaj, G.K. Singh, QRS detection using adaptive filters: a comparative study. ISA Trans. 66, 362–375 (2017)

    Article  Google Scholar 

  5. S.S. Mehta, D.A. Shete, N.S. Lingayat, V.S. Chouhan, K-means algorithm for the detection and delineation of QRS-complexes in electrocardiogram. IRBM 31, 48–54 (2010)

    Article  Google Scholar 

  6. S. Ari, M.K. Das, A. Chacko, ECG signal enhancement using S-transform. Comput. Biol. Med. 43, 649–660 (2013)

    Article  Google Scholar 

  7. B. Halder, S. Mitra, M. Mitra, Classification of complete myocardial infarction using rule-based rough set method and rough set explorer system. IETE J. Res. (2019). https://doi.org/10.1080/03772063.2019.1588175

    Article  Google Scholar 

  8. W.H. Jung, S.G. Lee, An arrhythmia classification method in utilizing the weighted KNN and the fitness rule. IRBM (2017). https://doi.org/10.1016/j.irbm.2017.04.002

    Article  Google Scholar 

  9. S.S. Mehta, N.S. Lingayat, Development of SVM based ECG pattern recognition technique. IETE J. Res. 54(1), 5–11 (2008)

    Article  Google Scholar 

  10. N.K. Jog, Electronics in Medicine and Biomedical Instrumentation, 2nd edn. (PHI, Delhi, 2013), pp. 85–109

    Google Scholar 

  11. A.K. Wadhwani, Delineation of ECG signal by using various method and techniques. Int. J. Mod. Eng. Res. 2(1), 662–665 (2013)

    Google Scholar 

  12. S.S. Mehta, N.S. Lingayat, SVM based QRS detection in electrocardiogram using signal entropy. IETE J. Res. 54(3), 231–240 (2008)

    Article  Google Scholar 

  13. M.S. Manikandan, K.P. Soman, A novel method for detecting R-peaks in electrocardiogram (ECG) signal. Biomed. Signal Process. Control 7(2), 118–128 (2012)

    Article  Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. R.S. Singh, B.S. Saini, R.K. Sunkaria, Times varying spectral coherence investigation of cardiovascular signals based on energy concentration in healthy young and elderly subjects by the adaptive continuous morlet wavelet transform. IRBM (2018). https://doi.org/10.1016/j.irbm.2017.12.004

    Article  Google Scholar 

  16. P.A. Wolf, R.D. Abbott, W.B. Kannel, Atrial fibrillation as an independent risk factor for stroke: the Framingham study. Stroke 22, 983–988 (1991)

    Article  Google Scholar 

  17. R.J. Martis, U.R. Acharya, H. Adeli, Current methods in electrocardiogram characterization. Comput. Biol. Med. 48, 133–149 (2014)

    Article  Google Scholar 

  18. D. Ge, N. Srinivasan, S.M. Krishnan, Cardiac arrhythmia classification using autoregressive modelling. Biomed. Eng. Online 1(5), 1–12 (2002)

    Google Scholar 

  19. B. Vuksanovic, M. Alhamdi, AR-based method for ECG classification and patient recognition. Int. J. Biom. Bioinform. 7(2), 74–92 (2013)

    Google Scholar 

  20. K. Padmavathi, Classification of ECG signal during atrial fibrillation using autoregressive modelling. Procedia Comput. Sci. 46, 53–59 (2015)

    Article  Google Scholar 

  21. I.I. Christov, Real time electrocardiogram QRS detection using combine adaptive threshold. Biomed. Eng. Online (2004). https://doi.org/10.1186/1475-925X-3-28

    Article  Google Scholar 

  22. P.S. Hamilton, W.J. Tompkin, Quantitative investigation of QRS detection rules using MIT/BIH arrhythmia database. IEEE Trans. Biomed. Eng. 33, 1157–1165 (1986)

    Article  Google Scholar 

  23. V. Gupta, M. Mittal, A novel method of cardiac arrhythmia detection in electrocardiogram signal. IJMEI (2020). https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijmei

  24. V. Gupta, M. Mittal, QRS complex detection using STFT, chaos analysis, and PCA in standard and real-time ECG databases. J. Inst. Eng. India Ser. B (2019). https://doi.org/10.1007/s40031-019-00398-9

    Article  Google Scholar 

  25. U.R. Acharya, O. Faust, N.A. Kadri, J.S. Suri, W. Yu, Automated identification of normal and diabetes heart rate signals using nonlinear measures. Comput. Biol. Med. 43, 1523–1529 (2013)

    Article  Google Scholar 

  26. V. Gupta, M. Mittal, A comparison of ECG signal pre-processing using FrFT, FrWT and IPCA for improved analysis. IRBM (2019). https://doi.org/10.1016/j.irbm.2019.04.003

    Article  Google Scholar 

  27. L.D.A. Valencia, J.I.G. Llorente, M.B. Velasco, G.C. Dominguez, Feature extraction from parametric time–frequency representations for heart murmur detection. Ann. Biomed. Eng. 38(8), 2716–2732 (2010)

    Article  Google Scholar 

  28. N. Neofytou, ECG event detection and recognition using time–frequency analysis. Master of Science in Biomedical Engineering. Department of Electrical and Computer Engineering, University of Cyprus, 2012

  29. I. Saini, D. Singh, A. Khosla, QRS detection using K-nearest neighbor algorithm (KNN) and evaluation on standard ECG databases. J. Adv. Res. 4(4), 331–344 (2013)

    Article  Google Scholar 

  30. H.M. Rai, A. Trivedi, K. Chatterjee, S. Shukla, R-peak detection using daubechies wavelet and ECG signal classification using radial basis function neural network. J. Inst. Eng. India Ser. B 95(1), 63–71 (2014)

    Article  Google Scholar 

  31. Z.U. Haque, R. Qureshiy, M. Nawazy, F.Y. Khuhawar, N. Tunioz, M. Uzairx, Analysis of ECG signal processing and filtering algorithms. Int. J Adv. Comput. Sci. Appl. (2019). https://doi.org/10.14569/ijacsa.2019.0100370

    Article  Google Scholar 

  32. M. Mortezaee, Z. Mortezaie, V. Abolghasemi, An improved SSA-based technique for EMG removal from ECG. IRBM 40, 62–68 (2019)

    Article  Google Scholar 

  33. L.B. Marinho, N.M.M. Nascimento, J.W.M. Souza, M.V. Gurgel, P.P.R. Filho, V.H.C. de Albuquerque, A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification. Future Gen. Comput. Syst. 97, 564–577 (2019)

    Article  Google Scholar 

  34. V. Gupta, M. Mittal, Electrocardiogram signals interpretation using chaos theory. J. Adv. Res. Dyn. Control Syst. 10(2), 2392–2397 (2018)

    Google Scholar 

  35. V. Gupta, M. Mittal, R-peak detection in ECG signal using Yule–Walker and principal component analysis. IETE J. Res. (2019). https://doi.org/10.1080/03772063.2019.1575292

    Article  Google Scholar 

  36. R. He, K. Wang, Q. Li, Y. Yuan, N. Zhao, Y. Liu, H. Zhang, A novel method for the detection of R-peaks in ECG based on K-nearest neighbors and particle swarm optimization. EURASIP J. Adv. Signal Process. (2017). https://doi.org/10.1186/s13634-017-0519-3

    Article  Google Scholar 

  37. M. Chakraborty, D. Ghosh, Quantitative assessment of arrhythmia using non-linear approach: a non-invasive prognostic tool. J. Inst. Eng. India Ser. B (2017). https://doi.org/10.1007/s40031-017-0307-3

    Article  Google Scholar 

  38. G.V. Van, K.V. Podmasteryev, Algorithm for detection the QRS complexes based on support vector machine. IOP Conf. Ser. J. Phys. Conf. Ser. 929, 1–5 (2017)

    Google Scholar 

  39. http://www.physionet.org. Accessed 7 Apr 2017

  40. J.P. Burg, A new analysis technique for time series data (NATO Advanced Study Institute on Signal Processing, Enschede, 1968)

    Google Scholar 

  41. I. Feigler, Time frequency analysis of ECG signals. Thesis, Experimental Biology Masarykova Universit

  42. M.D. Aloia, A. Longo, M. Rizzi, Noisy ECG signal analysis for automatic peak detection. Information (2019). https://doi.org/10.3390/info10020035

    Article  Google Scholar 

  43. A. Yazdani, T. Ebrahimi, U. Hoffmann, Classification of EEG signals using Dempster Shafer theory and a K-nearest neighbour classifier, in Proceedings of the 4th int IEEE EMBS Conference on Neural Engineering, Antalya, Turkey; 29 Apr–2 May 2009, pp. 327–330

  44. K. Zakkas, A complete guide to K-nearest neighbors with applications in python and R, 13 Jul (2016). https://kevinzakka.github.io/2016/07/13/knearest-neighbor/

  45. S. Rekik, N. Ellouze, Enhanced and optimal algorithm for QRS detection. IRBM (2016). https://doi.org/10.1016/j.irbm.2016.11.004

    Article  Google Scholar 

  46. M.P.S. Chawla, Parameterization and R-peak error estimations of ECG signals using independent component analysis. Comput. Math. Methods Med. 8(4), 263–285 (2007)

    Article  MathSciNet  Google Scholar 

  47. Z. Zidelmal, QRS detection based on wavelet coefficients. Comput. Methods Programs Biomed. 107(3), 490–496 (2012)

    Article  Google Scholar 

  48. R.J. Martis et al., ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed. Signal Process. Control 8(5), 437–448 (2013)

    Article  Google Scholar 

  49. A.K. Dohare, An efficient new method for the detection of QRS in electrocardiogram. Comput. Electr. Eng. 40(5), 1–9 (2013)

    Google Scholar 

  50. P. Kora, K.S.R. Krishna, ECG based heart arrhythmia detection using wavelet coherence and bat algorithm. Sens. Imaging 17, 12 (2016)

    Article  Google Scholar 

  51. P. Kora, ECG based myocardial infarction detection using hybrid firefly algorithm. Comput. Methods Programs Biomed. (2017). https://doi.org/10.1016/j.cmpb.2017.09.015

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. KIET Group of Institutions, Delhi-NCR, Ghaziabad, India

    Varun Gupta

  2. National Institute of Technology, Kurukshetra, Kurukshetra, India

    Monika Mittal

Authors
  1. Varun Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Monika Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Varun Gupta.

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

Gupta, V., Mittal, M. Efficient R-peak Detection in Electrocardiogram Signal Based on Features Extracted Using Hilbert Transform and Burg Method. J. Inst. Eng. India Ser. B 101, 23–34 (2020). https://doi.org/10.1007/s40031-020-00423-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40031-020-00423-2

Keywords

Search

Navigation