Arabian Journal for Science and Engineering

, Volume 44, Issue 8, pp 6679–6691 | Cite as

A Novel Approach to ECG R-Peak Detection

  • Amandeep Kaur
  • Alpana Agarwal
  • Ravinder Agarwal
  • Sanjay KumarEmail author
Research Article - Electrical Engineering


Electrocardiogram (ECG) signal processing and analysis is becoming more and more popular as it is useful in diagnosis and prognosis of human heart and clinically automatic machine estimation is based upon it. R-peak is the most important component in ECG beat and is widely used to investigate normal and abnormal subjects (patients). From the last few decades, R-peak detection in ECG has been the most challenging topic in the biomedical research. As QRS complex has high frequency in ECG as compared to other waves (P, T, U-wave), so majority of algorithms estimate QRS complex by either filtering or suppressing the lower frequency waves, including various artifacts like baseline wander, power line interference, and electromyograph noises. This paper demonstrates a new kind of ECG denoising algorithm based on self-convolution window (SCW) concept. The SCW based on Hamming window, herein referred to as Hamming self-convolution window, is used to design a new kind of filter which possesses negligible ripples in the stop band, as compared to the conventional window-based filters. This algorithm is validated on MIT-BIH arrhythmia database and the results outperform in terms of sensitivity, positive predictivity, and error rate obtained as 99.93%, 99.95%, and 0.117%, respectively, as compared to the other well-established works. The approach has also outperformed the results of well-established window-based filters (Hamming and Kaiser) in terms of reduced false negative, false positive, and error rate.


Convolution window Finite impulse response filter Hamming self-convolution window (HSCW) Kaiser window 


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The work was supported by Science and Engineering Research Board (SERB) (No. SB/S3/EECE/0149/2016), Department of Science and Technology (DST), Government of India, India.


  1. 1.
    Gacek, A.; Pedrycz, W.: ECG signal processing, classification and interpretation: a comprehensive framework of computational intelligence. Springer, Berlin (2011)Google Scholar
  2. 2.
    Sabherwal, P.; Agrawal, M.; Singh, L.: Automatic detection of the R peaks in single-lead ECG signal. Circuits Syst. Signal Process. 36(11), 4637–4652 (2017)CrossRefGoogle Scholar
  3. 3.
    Thakor, N.V.; Zhu, Y.S.: Application of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 38(8), 785–794 (1991)CrossRefGoogle Scholar
  4. 4.
    Razzaq, N.; Sheikh, S.A.A.; Salman, M.; Zaidi, T.: An intelligent adaptive filter for elimination of power line interference from high resolution electrocardiogram. IEEE Access. 4, 1676–1688 (2016)CrossRefGoogle Scholar
  5. 5.
    Zidelmal, Z.; Amirou, A.; Adnane, M.; Belouchrani, A.: QRS detection based on wavelet coefficients. Comput. Methods Programs Biomed. 107(3), 490–496 (2012)CrossRefGoogle Scholar
  6. 6.
    Cesari, M.; Mehlsen, J.; Mehlsen, A.B.; Sorensen, H.B.D.: A new wavelet-based ECG delineator for the evaluation of the ventricular innervation. IEEE J. Transl. Eng. Health Med. 5, 1–15 (2017)CrossRefGoogle Scholar
  7. 7.
    Bouaziz, F.; Boutana, D.; Benidir, M.: Multiresolution wavelet-based QRS complex detection algorithm suited to several abnormal morphologies. IET Signal Process. 8(7), 774–782 (2014)CrossRefGoogle Scholar
  8. 8.
    Aqil, M.; Jbari, A.; Bourouhou, A.: Adaptive ECG wavelet analysis for R-peaks detection. In: Proceedings of the IEEE International Conference on Electrical and Information Technologies (ICEIT), pp. 164–167 (2016)Google Scholar
  9. 9.
    Mahmoodabadi, S.Z.; Ahmadian, A.; Abolhasani, M.D.: ECG feature extraction using Daubechies wavelets. In: Proceedings of the Fifth International Conference on Visualization, Imaging and Image Processing, pp. 343–348 (2005)Google Scholar
  10. 10.
    Singh, B.N.; Tiwari, A.K.: Optimal selection of wavelet basis function applied to ECG signal denoising. Digit. Signal Process. 16(3), 275–287 (2006)CrossRefGoogle Scholar
  11. 11.
    Arzeno, N.; Deng, Z.D.; Poon, C.: Analysis of first-derivative based QRS detection algorithms. IEEE Trans. Biomed. Eng. 55(2), 478–484 (2008)CrossRefGoogle Scholar
  12. 12.
    Sahoo, S.; Biswal, P.; Das, T.; Sabut, S.: De-noising of ECG signal and QRS detection using Hilbert transform and adaptive thresholding. Proc. Technol. 25, 68–75 (2016)CrossRefGoogle Scholar
  13. 13.
    Phukpattaranont, P.: QRS detection algorithm based on the quadratic filter. Expert Syst. Appl. 42(11), 4867–4877 (2015)CrossRefGoogle Scholar
  14. 14.
    Sharma, T.; Sharma, K.K.: QRS complex detection in ECG signals using locally adaptive weighted total variation denoising. Comput. Biol. Med. 87, 187–199 (2017)CrossRefGoogle Scholar
  15. 15.
    Harris, F.H.: On the use of windows for harmonic analysis with the discrete Fourier transform. Proc. IEEE 66(1), 51–83 (1978)CrossRefGoogle Scholar
  16. 16.
    Reljin, I.S.; Reljin, B.D.; Papic, V.D.: Extremely flat-top windows for harmonic analysis. IEEE Trans. Instrum. Meas. 56(3), 1025–1041 (2007)CrossRefGoogle Scholar
  17. 17.
    Reljin I.; Reljin, B.: Signal processing by using new window functions generated by means of convolution. In: Proceedings of 9th ISTET, pp. 232–234 (1997)Google Scholar
  18. 18.
    Xianzhong, D.; Gretsch, R.: Quasi-synchronous sampling algorithm and its applications. IEEE Trans. Instrum. Meas. 43(2), 204–209 (1994)CrossRefGoogle Scholar
  19. 19.
    Zhang, J.; Liang, C.; Chen, Y.: A new family of windows–convolution windows and their applications. Sci. China Ser. E Technol. Sci. 48(4), 468–481 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Wen, H.; Teng, Z.; Guo, S.: Triangular self-convolution window with desirable sidelobe behaviors for harmonic analysis of power system. IEEE Trans. Instrum. Meas. 59(3), 543–552 (2010)CrossRefGoogle Scholar
  21. 21.
    Krishna, B.T.; Chandrasekhar, P.: Reduction of sidelobe level using convolutional windows. In: Proceedings of 1st International Conference in Emerging Technology Trends in Electronics, Communication and Networking (ET2ECN), pp. 1–5 (2012)Google Scholar
  22. 22.
    Ozaktas, H.M.; Zalevsky, Z.; Kutay, M.A.: The Fractional Fourier Transform with Applications in Optics and Signal Processing. Wiley, New York (2001)Google Scholar
  23. 23.
    Kumar, S.; Singh, K.; Saxena, R.: Analysis of Dirichlet and generalized “Hamming” window functions in the fractional Fourier transform domains. Signal Process. 91(3), 600–606 (2011)CrossRefzbMATHGoogle Scholar
  24. 24.
    Kumar, S.; Singh, K.; Saxena, R.: Closed-form analytical expression of fractional order differentiation in fractional Fourier transform domain. Circuits Syst. Signal Process. 32(4), 1875–1889 (2013)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Kumar, S.; Singh, K.; Saxena, R.: Caputo-based fractional derivative in fractional Fourier transform domain. IEEE J. Emerg. Sel. Top. Circuits Syst. 3(3), 300–307 (2013)Google Scholar
  26. 26.
    Kumar, S.: Analysis and design of non-recursive digital differentiators in fractional domain for signal processing applications. Ph.D. dissertation, Thapar University, Patiala, India (2014)Google Scholar
  27. 27.
    Rai, P.; Varaprasad, O.V.S.R.; Sarma, D.S.: An overview of power harmonic analysis based on triangular self convolution window. In: IEEE Conference on Power Systems, pp. 1–5 (2016)Google Scholar
  28. 28.
    MIT-BIH Arrhythmia Database (Massachusetts Institute of Technology, Biomedical Engineering Center, Cambridge, MA, 1992). Accessed 20 Mar 2018
  29. 29.
    Nuttall, A.H.: Some windows with very good sidelobe behavior. IEEE Trans. Acoust. Speech Signal Process. 29(1), 84–91 (1981)CrossRefGoogle Scholar
  30. 30.
    Prabhu, K.M.M.: Window Functions and their Applications in Signal Processing. CRC Press, Boca Raton (2013)CrossRefGoogle Scholar
  31. 31.
    Proakis, J.G.; Manolakis, D.G.: Digital Signal Processing, 3rd edn. MacMillan, New York (1996)Google Scholar
  32. 32.
    Meyer, C.; Gavela, J.F.; Harris, M.: Combining algorithms in automatic detection of QRS complexes in ECG signals. IEEE Trans. Inf. Technol. Biomed. 10(3), 468–475 (2006)CrossRefGoogle Scholar
  33. 33.
    Pan, J.; Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • Amandeep Kaur
    • 1
  • Alpana Agarwal
    • 1
  • Ravinder Agarwal
    • 2
  • Sanjay Kumar
    • 1
    Email author
  1. 1.Department of Electronics and Communication EngineeringThapar Institute of Engineering and TechnologyPatialaIndia
  2. 2.Department of Electronics and Instrumentation EngineeringThapar Institute of Engineering and TechnologyPatialaIndia

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