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R-Peak Detection using Daubechies Wavelet and ECG Signal Classification using Radial Basis Function Neural Network

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Abstract

This paper employed the Daubechies wavelet transform (WT) for R-peak detection and radial basis function neural network (RBFNN) to classify the electrocardiogram (ECG) signals. Five types of ECG beats: normal beat, paced beat, left bundle branch block (LBBB) beat, right bundle branch block (RBBB) beat and premature ventricular contraction (PVC) were classified. 500 QRS complexes were arbitrarily extracted from 26 records in Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, which are available on Physionet website. Each and every QRS complex was represented by 21 points from p1 to p21 and these QRS complexes of each record were categorized according to types of beats. The system performance was computed using four types of parameter evaluation metrics: sensitivity, positive predictivity, specificity and classification error rate. The experimental result shows that the average values of sensitivity, positive predictivity, specificity and classification error rate are 99.8%, 99.60%, 99.90% and 0.12%, respectively with RBFNN classifier. The overall accuracy achieved for back propagation neural network (BPNN), multilayered perceptron (MLP), support vector machine (SVM) and RBFNN classifiers are 97.2%, 98.8%, 99% and 99.6%, respectively. The accuracy levels and processing time of RBFNN is higher than or comparable with BPNN, MLP and SVM classifiers.

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References

  1. M.S.Thaler, The only EKG book you’ll ever need, Lippincott Williams and Wilkins, Edition 2006

  2. S. Osowski, T.H. Linh, ECG beat recognition using fuzzy hybrid neural network. IEEE Transactions on Biomedical Engineering 48, 1265–1271 (2001)

    Article  Google Scholar 

  3. M.R.Risk, J.F.Sobh, J.P.Saul, Beat detection and classification of ECG using self organizing maps, Proceedings of the Nineteenth International Conference of IEEEIEMBS, Chicago, IL USA, October 30-November 2, 1997

  4. T.Stamkopoulos, N.Maglaveras, K.Diamantaras, M.Strintzis, ECG Analysis using nonlinear PCA neural networks for ischemia detection, IEEE Transaction on signal process. 46(11), 3058–3067 (1998)

  5. P.de Chazal, B.G.Celler, R.B.Rei, Using wavelet coefficients for the classification of the electrocardiogram, Proceedings of the Twenty-second Annual EMBS International Conference, Chicago IL, July 23–28, 2000

  6. Z. Dokur, T. Olmez, ECG beat classification by a novel hybrid neural network. Computer Methods and Programs in Biomedicine 66, 167–181 (2001)

    Article  Google Scholar 

  7. N.Srinivasan, D.F.Ge, S.M.Krishnan, Autoregressive modelling and classification of cardiac arrhythmias, Proceedings of the Second Joint Conference Houston, TX, USA, October 23–26, 2002

  8. J.Zhou, Automatic detection of premature ventricular contraction using quantum neural networks, Proceedings of the Third IEEE Symposium on Bio-Informatics and Bio-Engineering, 2003

  9. S. I.Niwas, R.S.S.Kumari, V.Sadasivam, Artificial neural network based automatic cardiac abnormalities classification, Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications, 2005

  10. O.T.Inan, L.Giovangrandi, G.T.A. Kovacs, Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features, IEEE Transactions on Biomedical Engineering, 53(12), 2507–2515, 2006

  11. W.Jiang, S.G. Kong, Block-based neural networks for personalized ECG signal classification, IEEE Transactions on Neural Networks, 18(6), 1750–1761, 2007

  12. H.Hussain, L.L.Fatt, Efficient ECG signal classification using sparsely connected radial basis function neural network, Proceeding of the Sixth WSEAS International Conference on Circuits, Systems, Electronics, Control and Signal Processing, 412–416, December 2007

  13. F.Melgani, Y.Bazi, Classification of electrocardiogram signals with support vector machines and particle swarm optimization, IEEE Transactions on Information Technology in Biomedicine, 12(5), 667–677, 2008

  14. S.M. Jadhav, S.L.Nalbalwar, A.A.Ghatol, ECG arrhythmia classification using modular neural network model, IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES 2010), Kuala Lumpur, Malaysia, November 30–December 2, 2010

  15. M.Llamedo, J.P.Martınez, Heartbeat classification using feature selection driven by database generalization criteria, IEEE Transactions on Biomedical Engineering, 58(3), 616–625 2011

  16. V.Mai, I.Khalil, C.Meli, ECG biometric using multilayer perceptron and radial basis function neural networks, The Thirty-three Annual International Conference of the IEEE EMBS Boston, Massachusetts USA, August 30–September 3, 2011

  17. Y. Ozbay, R. Ceylan, B. Karlik, Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier. Expert Systems with Applications 38, 1004–1010 (2011)

    Article  Google Scholar 

  18. M.R. Homaeinezhad, S.A. Atyabi, E. Tavakkoli, H.N. Toosi, A. Ghaffari, R. Ebrahimpour, ECG arrhythmia recognition via a neuro-SVM-KNN hybrid classifier with virtual QRS image-based geometrical features. Expert Systems with Applications 39, 2047–2058 (2012)

    Article  Google Scholar 

  19. A. Martíneza, R. Alcaraza, J.J. Rieta, Ventricular activity morphological characterization: Ectopic beats removal in long term atrial fibrillation recordings. Computer Methods and Programs in Biomedicine 109, 283–292 (2013)

    Article  Google Scholar 

  20. H. H.de C.Junior, R.L.Moreno, T.C.Pimenta, P.C.Crepaldi, E.Cintra, A heart disease recognition embedded system with fuzzy cluster algorithm, Computer Methods and Programs in Biomedicine, 110, 447–454, 2013

  21. Z. Zidelmal, A. Amirou, D. Ould-Abdeslam, J. Merckle, ECG beat classification using a cost sensitive classifier. Computer Methods and Programs in Biomedicine 111, 570–577 (2013)

    Article  Google Scholar 

  22. H.M. Rai, ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier. Measurement 46(9), 3238–3246 (2013)

    Article  MathSciNet  Google Scholar 

  23. M.Misiti, Y.Misiti, Georges Oppenheim, Jean-Michel Poggi, Wavelet Toolbox for use with MATLAB, 1, March 1996

  24. Guler, E.D. Ubeyli, ECG beat classifier designed by combined neural network model. Pattern Recognition 38, 199–208 (2005)

    Article  Google Scholar 

  25. S. Mallat, A Wavelet Tour of Signal Processing (Academic Press, Burlington, MA, 1999)

    MATH  Google Scholar 

  26. R.O.Dude, P.E.H.D.G.Stork, Pattern Classification, Edition II, John Wiley, 2002

  27. Fundamental of Neural Networks, www.myreaders.info/html/artificial_intelligence.html

  28. M. Korürek, B. Dogan, ECG beat classification using particle swarm optimization and radial basis function neural network. Expert Systems with Applications 37, 7563–7566 (2010)

    Article  Google Scholar 

  29. M.Hajek, Neural Networks, 2005

  30. H.Yu, T.Xie, S.Paszczyñski, B.M.Wilamowski, Advantages of Radial Basis Function Networks for Dynamic System Design, IEEE Transactions on Industrial Electronics, 58(12), 5438–5450 December 2011

  31. J. Park, I.W. Sandberg, Universal approximation using radial basis-function networks. Neural Computation 3(2), 246–257 (1991)

    Article  Google Scholar 

  32. J. Moody, C.J. Darken, Fast learning in networks of locally-tuned processing units. Neural Computation 1(2), 281–294 (1989)

    Article  Google Scholar 

  33. B.M.Wilamowski, R.C.Jaeger, Implementation of RBF type networks by MLP networks, Proceedings of IEEE International Conference Neural Networks, Washington, DC, 1670–1675, June 3-6, 1996

  34. M.J.D. Powell, The theory of radial basis functions approximation. Advances of Numerical Analysis (Clarendon Press, Oxford, 1992), pp. 105–210

    Google Scholar 

  35. F. Girosi, Some extensions of radial basis functions and their applications in artificial intelligence. Computers and Mathematics with Applications 24(12), 61–80 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  36. N. Kasabov, Foundations of Neural networks, Fuzzy Systems and Knowledge Engineering, MIT Press, 1996

  37. A.P. Topchy, O.A. Lebedko, V.V. Miagkikh, N.K. Kasabov, Adaptive training of radial basis function networks based on cooperative evolution and evolutionary programming (Progress in Connectionist-Based Information Systems, Springer, 1998), pp. 253–258

    Google Scholar 

  38. Physiobank Archive Index, MIT-BIH Arrhythmia Database, http://www.physionet.org/physiobank/database

  39. R.Mark, G.Moody, MIT-BIH arrhythmia database directory, http://ecg.mit.edu/dbinfo.html

  40. H.M.Rai, A.Trivedi, De-noising of ECG waveforms using multiresolution wavelet transform, International Journal of Computer Application, 45(18), 25–30 May 2012

  41. S. Pal, M. Mitra, Detection of ECG characteristic points using multiresolution wavelet analysis based selective coefficient method. Measurement 43, 255–261 (2010)

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Dronacharya College of Engineering, Gurgaon, India

    H. M. Rai

  2. Department of Electrical Engineering, Jabalpur Engineering College, Jabalpur, Madhya Pradesh, India

    A. Trivedi & S. Shukla

  3. Indian School of Mines, Dhanbad, India

    K. Chatterjee

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  1. H. M. Rai
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  2. A. Trivedi
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  4. S. Shukla
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Correspondence to H. M. Rai.

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Rai, H.M., Trivedi, A., Chatterjee, K. et al. R-Peak Detection using Daubechies Wavelet and ECG Signal Classification using Radial Basis Function Neural Network. J. Inst. Eng. India Ser. B 95, 63–71 (2014). https://doi.org/10.1007/s40031-014-0073-4

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  • DOI: https://doi.org/10.1007/s40031-014-0073-4

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