A Nonparametric Derivative-Based Method for R Wave Detection in ECG


QRS detection is very important in cardiovascular disease diagnosis and ECG (electrocardiogram) monitor, because it is the precondition of the calculation of correlative parameters and diagnosis. This paper presents a non-parametric derivative-based method for R wave detection in ECG signal. This method firstly uses a digital filter to cut out noises from ECG signals, utilizes local polynomial fitting that is a non-parametric derivative-based method to estimate the derivative values, and then selects appropriate thresholds by the difference, and the algorithm adaptively adjusts the size of thresholds periodically according to the different needs. Afterwards, the position of R wave is detected by the estimation of the first-order derivative values with nonparametric local polynomial statistical model. In addition, in order to improve the accuracy of detection, the method of redundant detection and missing detection are applied in this paper. The clinical experimental data are used to evaluate the effectiveness of the algorithm. Experimental results show that the method in the process of the detection of R wave is much smoother, compared with differential threshold algorithm and it can detect the R wave in the ECG signals accurately.

Share and Cite:

Su, L. , Sun, M. , Li, C. and Peng, X. (2014) A Nonparametric Derivative-Based Method for R Wave Detection in ECG. Journal of Computer and Communications, 2, 26-38. doi: 10.4236/jcc.2014.212004.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Bellout, H., Neustupa, J. and Penel, P. (2004) On the Navier-Stokes Equation with Boundary Conditions Based on Vorticity. Mathematische Nachrichten, 269, 59-72.
[2] Girault, V. and Raviart, P.A. (1979) Finite Element Approximation of the Navier-Stokes Equations. Lecture Notes in Mathematics, Springer Verlag, Berlin, 749.
[3] Hopf, E. (1951) Uber die Anfangswertaufgabe für die Hydrodynamischen Grundgleichungen. Mathematische Nachrichten, 4, 213-231. http://dx.doi.org/10.1002/mana.3210040121
[4] He, P., Kong, G., Su. Z. and Kato, T. (1972) Non-Stationary Flows of Viscous and Ideal Fluids in . Journal of Functional Analysis, 9, 296-305. http://dx.doi.org/10.1016/0022-1236(72)90003-1
[5] Serrin, J. (1962) On the Interior Regularity of Weak Solutions of the Navier-Stokes Equations. Archive for Rational Mechanics and Analysis, 9, 187-195.
[6] Su, L., Zhao, G.L. and Li, D.M. (2005) Study of Algorithms of QRS Complexes Detection in Electrocardiogram Signalse. Journal of Harbin Engineering University, 26, 513-517.
[7] Zhang, S., Wu, Z.G. and Li, Z. (2008) An Adaptive Detection of ECGs R-Wave. Journal of Sichuan University (Natural Science Edition), 45, 498-502.
[8] Tian, Y.Q., Sun, X.J., Hao, D.M., et al. (2001) Research on Automatic Diagnosis System of Electrocardiogram. Chinese Journal of Medical Instrumentation, 25, 204-206.
[9] Friesen, G.M., Jannett, T.C., Jadallah, M.A., Yates, S.L., Quint, S.R. and Nagle, H.T. (1990) A Comparison of the Noises Sensitivity of Nine QRS Detection Algorithms. IEEE Transactions on Biomedical Engineering, 37, 85-98.
[10] Su, L.Y., Kong, T., et al. (2010) R-Wave Detection of ECG Signals Submerged in Fractal Noises. Journal of Chongqing University of Technology (Natural Science), 24, 86-90.
[11] Saxena, S.C., Kumar, V. and Hamde, S.T. (2002) QRS Detection Using New Wavelets. Journal of Medical Engineering & Technology, 26, 7-15. http://dx.doi.org/10.1080/03091900110096038
[12] Legarreta, I.R., Addison, P.S., Grubb, N., et al. (2003) R-Wave Detection Using Continuous Wavelet Modulus Maxima. IEEE international Conference on Computers in Cardiology, 30, 565-568.
[13] Mourad, T., Akram, A., Lotfi, S. and Adnane, C. (2011) New Method of R-Wave Detection by Continuous Wavelet Transform. Signal Processing: An International Journal, 5, 165-173.
[14] Li, C.W., Zheng, C.X. and Tai, C.F. (1995) Detection of ECG Characteristic Points Using Wavelet Transforms. IEEE Transactions on Biomedical Engineering, 42, 21-28.
[15] Xu, X.M. and Liu, Y. (2006) Adaptive Threshold for QRS Complex Detection Based on Wavelet Transform. IEEE Engineering in Medicine and Biology Society, 7, 7281-7284.
[16] Mallat, S. (1989) A Theory of Multiresolution Signals Decomposition: The Wavelet Transform. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 674-693.
[17] Afonso, V.X., Tompkins, W.J., Nguyen, T.Q. and Luo, S. (1999) ECG Beat Detection Using Filter Banks. IEEE Transactions on Biomedical Engineering, 46, 192-202. http://dx.doi.org/10.1109/10.740882
[18] Chen, H.C. and Chen, S.W. (2003) A Moving Average Based Filtering System with Its Application to Real-Time QRS Detection. Computers in Cardiology, 30, 585-588.
[19] Li, X.Y., Wang, T., Feng, H.Q. and Zhan, C.G. (2000) Removal of ECG Baseline Drift Using Adaptive Filter Based on Wavelet Transform. Journal of China University of Science and Technology, 30, 450-454.
[20] Chen, Y.Q., Li, G., Ye, W.Y., et al. (2002) An Improved Algorithm of Adaptive Coherent Model in the Application of Electrocardiogram. Signal Processing, 18, 244-248.
[21] Chen, X.M., Lin, J.S. and Zhang, Z.G. (1999) An Improved Template Matching Method for High Resolution ECG. Chinese Journal of Biomedical Engineering, 18, 89-96.
[22] Ng, F., Mora, F., Wong, S., et al. (1997) Comparison between Neural-Network-Based Adaptive Filtering and Wavelet Transform for ECG Characteristic Points Detection. IEEE Engineering in Medicine and Biology Society, 1, 272-274.
[23] Xie, Q.Z., Hu, Y.H. and Tompkins, W.J. (1992) Neural Network Based Adaptive Matched Filtering of QRS Detection. IEEE Transactions on Biomedical Engineering, 39, 317-329.
[24] Hudson, D.L., Cohen, M.E. and Anderson, M.F. (1991) Use of Neural Network Techniques in a Medical Expert System. International Journal of Intelligent Systems, 6, 213-223.
[25] Zhang, J.Z., Zhang, L.X., Wei, D.X. and Zhang, G.L. (2008) Research on ECG Automatic Classification Used Neural Network K. Beijing Biomedical Engineering, 27, 41-43.
[26] Gao, Y. and Hu, Y. (2001) An ECG Waves Separation Technique Based on Mathematical Morphology. Journal of Biomedical Engineering, 18, 55-59.
[27] Trahanias, P.E. (1993) An Approach to QRS Complex Detection Using Mathematical Morphology. IEEE Transactions on Biomedical Engineering, 40, 201-205.
[28] Chen, Y.L. and Duan, H.L. (2007) The QRS Wave Detection Based on Mathematical Morphology and Extraction of Signals Envelope. Chinese Journal of Biomedical Engineering, 26, 332-335.
[29] Tian, X.Z., Yang, J. and Huang, L.Y. (2012) Morphological Filter to Remove Power-Line Interference and Baseline Wander in Electrocardiogram. Computer Engineering & Applications, 48, 124-126.
[30] Fan, J.Q. and Yao, Q.W. (2003) Nonlinear Time Series: Nonparametric and Parametric Methods. Springer, New York.
[31] Su, L.Y. (2010) Prediction of Multivariate Chaotic Time Series with Local Polynomial Fitting. Computers & Mathematics with Applications, 59, 737-744. http://dx.doi.org/10.1016/j.camwa.2009.10.019
[32] Jackson, R.H., Wu, A.C.F. and Verboncoeur, J.P. (2012) Numerical Solution of the Cylindrical Poisson Equation Using the Local Taylor Polynomial Technique. Journal of Computational Physics, 231, 5421-5442.
[33] Su, L.Y., Zhao, Y.Y., Yan, T.S. and Li, F.L. (2012) Local Polynomial Estimation of Heteroscedasticity in a Multivariate Linear Regression Model and Its Applications in Economics. PLoS ONE, 7, e43719.
[34] Su, L.Y., Zhao, Y.Y. and Yan, T.T. (2012) Two-Stage Method Based on Local Polynomial Fitting for a Linear Heteroscedastic Regression Model and Its Application in Economics. Discrete Dynamics in Nature and Society, 2012, Article ID: 696927.
[35] Martins-Filho, C. and Saraiva, P. (2012) On Asymptotic Normality of the Local Polynomial Regression Estimator with Stochastic Bandwidths. Communications in Statistics-Theory and Methods, 41, 1052-1068. http://dx.doi.org/10.1080/03610926.2010.535632
[36] Su, L.Y., Ma, Y.J. and Li, J.J. (2010) Application of Local Polynomial Estimation in Suppressing Strong Chaotic Noise. Chinese Physics B, 21, Article ID: 020508.
[37] Su, L.Y. and Li, F.L. (2010) Deconvolution of Defocused Image with Multivariate Local Polynomial Regression and Iterative Wiener Filtering in DWT Domain. Mathematical Problems in Engineering, 2010, Article ID: 605241.
[38] Su, L.Y., Yan, T.S., Zhao, Y.Y. and Li, F.L. (2012) Local Polynomial Regression Solution for Partial Differential Equations with Initial and Boundary Values. Discrete Dynamics in Nature and Society, 2012, Article ID: 201678.
[39] Su, L.Y. (2011) Multivariate Local Polynomial Regression with Application to Shenzhen Component Index. Discrete Dynamics in Nature and Society, 2011, Article ID: 930958.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.