Skip to main content
SpringerLink
Log in

Wavelet De-Noising and Genetic Algorithm-Based Least Squares Twin SVM for Classification of Arrhythmias

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

Abstract

The automatic detection of cardiac arrhythmias is a challenging task since the small variations in electrocardiogram (ECG) signals cannot be distinguished by the human eye. We propose a fast recognition method to diagnose heart diseases that is less time consuming and achieves better performance by combining wavelet de-noising with a genetic algorithm (GA)-based least squares twin support vector machine (LSTSVM). First, adaptive wavelet de-noising is employed for noise reduction. Second, power spectral density in combination with timing interval features is extracted to evaluate the classifier. Finally, a GA, particle swarm optimization (PSO), and chaotic PSO are compared for parameter optimization of the proposed directed acyclic graph LSTSVM multiclass classifiers. ECG heartbeats taken from the MIT-BIH arrhythmia database are used to examine the proposed method and other traditional classifiers such as multilayer perception, probabilistic neural network, learning vector quantization, extreme learning machine, SVM, and current TWSVMs. Number of our training samples is <3.2 % of all samples. Our proposed method demonstrates a high classification accuracy of 99.1403 % with low ratio of training and testing sample sizes; furthermore, it achieves a more rapid training and testing time of 0.2044 and 55.7383 s, respectively.

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

Similar content being viewed by others

References

  1. E. Alickovic, A. Subasi, Effect of multiscale PCA de-noising in ECG beat classification for diagnosis of cardiovascular disease. Circuits Syst.Signal Process. 34(2), 513–533 (2015)

    Article  Google Scholar 

  2. S.P. Arjunan, D.K. Kumar, G.R. Naik, A machine learning based method for classification of fractal features of forearm sEMG using Twin Support Vector Machines. International Conference of the IEEE Engineering in Medicine & Biology Society, pp. 513–533(2010)

  3. G.C. Clifford, F. Azuaje, P. Mcsharry, Advanced Methods and Tools for ECG Data Analysis (Artech House, London, 2006)

    Google Scholar 

  4. S. Comani, D. Mantini, G.L.S. Alleva, G.L. Romani, Fetal magnetocardiographic mapping using independent component analysis. Physiol. Meas. 25(6), 1459–1472 (2004)

    Article  Google Scholar 

  5. S. Comani, D. Mantinic, P. Pennesi, A. Lagatta, G. Cancellieri, Independent component analysis:fetal signal reconstruction from magnetocardiographic recordings. Comput. Methods Progr. Biomed. 75(2), 163–177 (2004)

    Article  Google Scholar 

  6. G. Clifford, L. Tarassenko, One-pass training of optimal architecture auto-associative neural network for detecting ectopic beats. Electron Lett. 37(18), 1126–1127 (2001)

    Article  Google Scholar 

  7. W.J. Chen, Y.H. Shao, N. Hong, Laplacian smooth twin support vector machine for semi-supervised classification. Int. J. Mach. Learn. Cybern. 5(3), 459–468 (2014)

    Article  Google Scholar 

  8. W.J. Chen, Y.H. Shao, C.N. Li, N.Y. Deng, MLTSVM: a novel twin support vector machine to multi-label learning. Pattern Recognit. 52, 61–74 (2016)

    Article  Google Scholar 

  9. S.G. Chang, B. Yu, M. Vetterli, Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9(9), 1532–1546 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  10. M.P.S. Chawla, H.K. Verma, V. Kumar, RETRACTED: a new statistical PCA-ICA algorithm for lacation of R-peaks in ECG. Int. J. Cardiol. 129(1), 146–148 (2008)

    Article  Google Scholar 

  11. S. Ding, X. Hua, Recursive least squares projection twin support vector machines for nonlinear classification. Neurocomputing 130(3), 3–9 (2014)

    Google Scholar 

  12. D.L. Donoho, J.M. Johnstone, Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3), 425–455 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  13. A. Ebrahimzadeh, A. Khazaee, Detection of premature ventricular contractions using MLP neural networks:a comparative study. Measurement 43(1), 103–112 (2010)

    Article  Google Scholar 

  14. G.H. Golub, C.F. VanLoan, Matrix Computations, 3rd edn. (John Hopkins University Press, London, 1996)

    Google Scholar 

  15. H.V. Huikuri, A. Castellanos, R.J. Myerburg, Sudden death due to cardiac arrhythmias. N. Engl. J. Med. 345(12), 1473–1482 (2001)

    Article  Google Scholar 

  16. 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 QRS image-based geometrical feature. Expert Syst. Appl. 39(2), 2047–2058 (2012)

    Article  Google Scholar 

  17. H.J. Huang, S.F. Ding, Z.Z. Shi, Primal least squares twin support vector regression. J. Zhejiang Univ. Sci. C 9(9), 722–732 (2013)

    Article  Google Scholar 

  18. Y.H. Hu, S. Palreddy, W.J. Tompkins, A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Trans. Biomed. Eng. 44(9), 891–900 (1997)

    Article  Google Scholar 

  19. O.T. Inan, L. Giovangrandi, G.T. Kovacs, Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Trans. Biomed. Eng. 53(12), 2507–2515 (2006)

    Article  Google Scholar 

  20. R. Jayadeva, S. Khemchandani, Chandra, Twin support vector machines for pattern classification. IEEE Trans. Pattern Anal. Mach. Intell. 29(5), 905–910 (2007)

    Article  MATH  Google Scholar 

  21. K. Kobayashi, Y. Uchikawa, T. Simizu, K. Nakai, M. Yoshizawa, The rejection of magnetic noise from the wire using independent component analysis for magnetocardiogram. IEEE Trans. Magn. 41(10), 4152–4154 (2005)

    Article  Google Scholar 

  22. M. Kaur, A.S. Arora, Classification of ECG signals using LDA with factor analysis method as feature reduction technique. J. Med. Eng. Technol. 36(8), 411–420 (2012)

    Article  Google Scholar 

  23. A. Khazaee, A.E. Zadeh, ECG beat classification using particle swarm optimization and support vector machine. Front. Comput. Sci. 8(2), 217–231 (2014)

    Article  MathSciNet  Google Scholar 

  24. A. Khazaee, A. Ebrahimzadeh, Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features. Biomed. Signal Process. Control. 5(4), 252–263 (2010)

    Article  Google Scholar 

  25. M.A. Kumar, M. Gopal, Least squares twin support vector machines for pattern classification. Expert Syst. Appl. 36(4), 7535–7543 (2009)

    Article  Google Scholar 

  26. S. Luo, P. Johnston, A review of electrocardiogram filtering. J. Electrocardiol. 43(6), 486–496 (2010)

    Article  Google Scholar 

  27. H.Q. Li, X.F. Wang, L. Chen, E.B. Li, Denoising and R-peak detection of electrocardiogram signal based on EMD and improved approximate envelope. Circuits Syst. Signal Process. 33(4), 1261–1276 (2014)

    Article  Google Scholar 

  28. M. Llamedo, J.P. Martinez, Heartbeat classification using feature selection driven by database generalization criteria. Trans. Biomed. Eng. 58(3), 616–625 (2011)

    Article  Google Scholar 

  29. Y. Lee, J. Lee, Binary tree optimization using genetic algorithm for multiclass support vector machine. Expert Syst. Appl. 42(8), 3843–3851 (2015)

    Article  Google Scholar 

  30. R. Leardi, 3-Genetic Algorithms in Feature Selection (Academic Press, New York, 1996)

    Book  Google Scholar 

  31. J.G. Murphy, M.A. Lloyd, Mayo Clinic Cardiology: Concise Textbook, Informa Healthcare USA. Rochester, 3rd edn. (Mayo Clinic Scientific Press, New York, 2007)

    Google Scholar 

  32. S. Moein, An MLP neural network for ECG noise removal based on Kalman filter. Adv. Exp. Med. Biol. 680(680), 109–116 (2010)

    Article  Google Scholar 

  33. N. Mariyappa, S. Sengottuvel, C. Parasakthi, K. Gireesan, M.P. Janawadkar, T.S. Radhakrishnan, C.S. Sundar, Baseline drift removal and denoising of MCG data using EEMD: role of noise amplitude and the thresholding effect. Med. Eng. Phys. 36(10), 1266–1276 (2014)

    Article  Google Scholar 

  34. R.J. Martis, U.R. Acharya, K.M. Mandana, A.K. Ray, C. Chakraborty, Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Syst. Appl. 39(14), 11792–11800 (2012)

    Article  Google Scholar 

  35. F. Melgani, Y. Bazi, Classification of electrocardiogram signals with support vector machines and particle swarm optimization. IEEE Trans. Inf. Technol. Biomed. 12(5), 667–677 (2008)

    Article  Google Scholar 

  36. S. Mahmoodabadi, A. Ahmadian, M. Abolhasani, M. Eslami, J. Bidgoli, ECG feature extraction based on multiresolution wavelet transform. 27th Annual International Conference of the Engineering in Medicine and Biology. Society. 4, 3902–3905 (2005)

  37. S. Maldonado, J. Lopez, M. Carrasco, A second-order cone programming formulation for twin support vector machines. Appl. Intell. 2, 1–12 (2016)

    Article  Google Scholar 

  38. R.P. Narwaria, S. Verma, P.K. Singhal, Removal of baseline wander and power line interference from ECG Signal-a survey approach. Int. J. Electron Eng. 3, 107–111 (2011)

    Google Scholar 

  39. G.R. Naik, D.K. Kumar, Jayadeva, Twin SVM for gesture classification using the surface electromyogram. IEEE Trans. Inf. Technol. Biomed. 14(2), 301–308 (2010)

    Article  Google Scholar 

  40. G.R. Naik, D.K. Kumar, Hybrid independent component analysis and twin support vector machine learning scheme for subtle gesture recognition. Biomed. Tech. 55(5), 301–307 (2010)

    Article  Google Scholar 

  41. J. Park, K. Kang, Intelligent classification of heartbeats for automated real-time ECG monitoring. Telemed. E-Health 20(12), 1069–1077 (2014)

    Article  Google Scholar 

  42. J. Park, K. Kang, PcHD: personalized classification of heartbeat types using a decision tree. Comput. Biol. Med. 54, 79–88 (2014)

    Article  Google Scholar 

  43. X. Peng, D. Xu, Robust minimum class variance twin support vector machine classifier. Neural Comput. Appl. 22(5), 999–1011 (2013)

    Article  Google Scholar 

  44. Z. Qi, Y. Tian, Y. Shi, Robust twin support vector machine for pattern classification. Pattern Recognit. 46(1), 305–316 (2013)

    Article  MATH  Google Scholar 

  45. I. Saini, D. Singh, A. Khosla, Electrocardiogram beat classification using empirical mode decomposition and multiclass directed acyclic graph support vector machine. Comput. Electr. Eng. 40(5), 1774–1787 (2014)

    Article  Google Scholar 

  46. Y.H. Shao, C.H. Zhang, X.B. Wang, N.Y. Deng, Improvements on twin support vector machines. IEEE Trans. Neural Netw. 22(6), 962–968 (2011)

    Article  Google Scholar 

  47. Y.H. Shao, W.J. Chen, Z. Wang, Weighted linear loss twin support vector machine for large-scale classification. Knowl.-Based Syst. 73, 276–288 (2015)

    Article  Google Scholar 

  48. J.S. Sartakhti, H. Afrabandpey, M. Saraee, Simulated annealing least squares twin support vector machine(SA-LSTSVM)for pattern classification. Soft Comput. 20(2), 1–13 (2016)

    Google Scholar 

  49. D. Tomar, S. Agarwal, Twin support vector machine: a review from 2007 to 2014. Egypt. Inform. J. 16(1), 55–69 (2015)

    Article  Google Scholar 

  50. Y.J. Tian, Q. Zhang, Y. Ping, Large-scale linear nonparallel support vector machines solver. Neurocomputing 138(22), 114–119 (2014)

    Article  Google Scholar 

  51. E.D. Übeyli, ECG beats classification using multiclass support vector machines with error correcting output codes. Digit. Signal Process. 17(3), 675–684 (2007)

    Article  Google Scholar 

  52. J.T. Willerson, H. Wellens, J.N. Cohn, D.R. holmes, Cardiovascular Medicine, 3rd edn. (Springer, London, 2007)

    Book  Google Scholar 

  53. Z. Wang, Y.H. Shao, T.R. Wu, A GA based model selection for smooth twin parametric-margin support vector machine. Pattern Recognit. 46(8), 2267–2277 (2013)

    Article  MATH  Google Scholar 

  54. T.H. Wang, D.Y. Zhao, Y.S. Feng, Two stage multiple kernel learning with multiclass kernel polarization. Knowl. Based Syst. 48(2), 10–16 (2013)

    Article  Google Scholar 

  55. S.N. Yu, Y.H. Chen, Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recognit. Lett. 28(10), 1142–1150 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, No. 10, Xitu Street, Haidian District, Beijing, 100876, China

    Duan Li, Hongxin Zhang & Mingming Zhang

  2. School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo, 454000, China

    Duan Li

Authors
  1. Duan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongxin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingming Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Duan Li.

Additional information

This research was supported by the National Natural Science Foundation of China (61571063).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, D., Zhang, H. & Zhang, M. Wavelet De-Noising and Genetic Algorithm-Based Least Squares Twin SVM for Classification of Arrhythmias. Circuits Syst Signal Process 36, 2828–2846 (2017). https://doi.org/10.1007/s00034-016-0439-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-016-0439-8

Keywords

Search

Navigation