ECG arrhythmia classification using artificial intelligence and nonlinear and nonstationary decomposition

  • Fakheraldin Y. O. Abdalla
  • Longwen Wu
  • Hikmat Ullah
  • Guanghui Ren
  • Alam Noor
  • Yaqin ZhaoEmail author
Original Paper


ECG signals reflect all the electrical activities of the heart. Consequently, it plays a key role in the diagnosis of the cardiac disorder and arrhythmia detection. Based on tiny alterations in the amplitude, duration and morphology of the ECG, computer-aided diagnosis has become a recognized approach to classifying the heartbeats of different types of arrhythmia. In this study, a classification approach was developed based on the non-linearity and nonstationary decomposition methods due to the nature of the ECG signal. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to obtain intrinsic mode functions (IMFs). Established on those IMFs, four parameters have been computed to construct the feature vector. Average power, coefficient of dispersion, sample entropy and singular values have been calculated as parameters from the first six IMFs. Then, ANN has been adopted to apply the feature vector using them and classify five different arrhythmia heartbeats downloaded from Physionet in the MIT–BIH database. To evaluate the performance of the proposed method and compare it with previous algorithms, confusion matrix, sensitivity (SEN), specificity (SPE), accuracy (ACC) and ROC have been used. It has been found that performance from the CEEMDAN and ANN is better than all existing methods, where the SEN is 99.7%, SPE is 99.9%, ACC is 99.9%, and ROC is 01.0%.


CEEMDAN EEMD ANN Feature extraction 



This paper is supported by the National Natural Science Foundation of China, China (Grant Number: 61671185).


  1. 1.
    Mendis, B.N.P.P.S.: Global Atlas on Cardiovascular Disease Prevention and Control. World Health Organization, Geneva (2011)Google Scholar
  2. 2.
    Al-Naser, M., Soderstrom, U.: Reconstruction of occluded facial images using asymmetrical principal component analysis. Integr. Comput. Aided Eng. 19, 273–283 (2012)CrossRefGoogle Scholar
  3. 3.
    Duda, P.H.R., Stork, D. (eds.): Pattern Classification. Wiley, New York (2001)zbMATHGoogle Scholar
  4. 4.
    Martis, R.J., Chakraborty, C., Ray, A.K.: A two-stage mechanism for registration and classification of ECG using Gaussian mixture model. Pattern Recogn. 42, 2979–2988 (2009)CrossRefzbMATHGoogle Scholar
  5. 5.
    Khazaee, A., Ebrahimzadeh, A.: Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features. Biomed. Signal Process. Control 5, 252–263 (2010)CrossRefGoogle Scholar
  6. 6.
    Alajlan, N., Bazi, Y., Melgani, F., Malek, S., Bencherif, M.A.: Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods. SIViP 8, 931–942 (2014)CrossRefGoogle Scholar
  7. 7.
    Desai, U., Martis, R.J., Nayak, C.G., Seshikala, G., Sarika, K., Shetty, R.K.: Decision support system for arrhythmia beats using ecg signals with DCT, DWT and EMD methods: a comparative study. J. Mech. Med. Biol. 16, 1640012 (2016)CrossRefGoogle Scholar
  8. 8.
    Martis, R.J., Acharya, U.R., Adeli, H.: Current methods in electrocardiogram characterization. Comput. Biol. Med. 48, 133–149 (2014)CrossRefGoogle Scholar
  9. 9.
    Fathi, A., Faraji-kheirabadi, F.: ECG compression method based on adaptive quantization of main wavelet packet subbands. SIViP 10, 1433–1440 (2016)CrossRefGoogle Scholar
  10. 10.
    Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., et al.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 25, 65 (2019)CrossRefGoogle Scholar
  11. 11.
    ShraddhaSingh, S.K.P., Pawar, U., Janghel, R.R.: Classification of ECG arrhythmia using recurrent neural networks. Proc. Comput. Sci. 132, 1290 (2018)CrossRefGoogle Scholar
  12. 12.
    Norden, Z.S., Huang, E., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-H., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. R Soc 454, 903 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Wu, Z., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1, 1–41 (2009)CrossRefGoogle Scholar
  14. 14.
    Torres, M.E., Colominas, M.A., Schlotthauer, G., Flandrin, P.: IEEE: a complete ensemble empirical mode decomposition with adaptive noise. In: 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 4144–4147 (2011)Google Scholar
  15. 15.
    Lovie, P.: Coefficient of variation. In: Encyclopedia of Statistics in Behavioral Science, vol. 1, pp. 2. (2005)Google Scholar
  16. 16.
    Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, H2039–H2049 (2000)CrossRefGoogle Scholar
  17. 17.
    Chang, C.-D., Wang, C.-C., Jiang, B.C.: Singular value decomposition based feature extraction technique for physiological signal analysis. J. Med. Syst. 36, 1769–1777 (2012)CrossRefGoogle Scholar
  18. 18.
    Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, In: Informedness, Markedness and Correlation (2011)Google Scholar
  19. 19.
    Zaplata, F., Kasal, M.: IEEE. In: SDR Implementation for DCF77 (2013)Google Scholar
  20. 20.
    Kutlu, Y., Kuntalp, D.: A multi-stage automatic arrhythmia recognition and classification system. Comput. Biol. Med. 41, 37–45 (2011)CrossRefGoogle Scholar
  21. 21.
    Kutlu, Y., Kuntalp, D.: Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients. Comput. Methods Progr. Biomed. 105, 257–267 (2012)CrossRefGoogle Scholar
  22. 22.
    Das, M.K., Ari, S.: ECG beats classification using mixture of features. Int. Sch. Res. Not. 2014, 178436 (2014)Google Scholar
  23. 23.
    Elhaj, F.A., Salim, N., Harris, A.R., Swee, T.T., Ahmed, T.: Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput. Methods Progr. Biomed. 127, 52–63 (2016)CrossRefGoogle Scholar
  24. 24.
    Rajesh, K.N.V.P.S., Uhuli, R.: Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine. Comput. Biol. Med. 87, 271–284 (2017)CrossRefGoogle Scholar
  25. 25.
    Rajesh, K.N.V.P.S., Dhuli, R.: Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier. Biomed. Signal Process. Control 41, 242–254 (2018)CrossRefGoogle Scholar
  26. 26.
    Martis, R.J., Acharya, U.R., Min, L.C.: ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed. Signal Process. Control 8, 437–448 (2013)CrossRefGoogle Scholar
  27. 27.
    Martis, R.J., Acharya, U.R., Lim, C.M., Suri, J.S.: Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework. Knowl. Based Syst. 45, 76–82 (2013)CrossRefGoogle Scholar
  28. 28.
    Li, P., Liu, C., Wang, X., Zheng, D., Li, Y., Liu, C.: A low-complexity data-adaptive approach for premature ventricular contraction recognition. SIViP 8, 111–120 (2014)CrossRefGoogle Scholar
  29. 29.
    Hammad, M., Maher, A., Wang, K., Jiang, F., Amrani, M.: Detection of abnormal heart conditions based on characteristics of ECG signals. Measurement 125, 634–644 (2018)CrossRefGoogle Scholar
  30. 30.
    Sahoo, S., Kanungo, B., Behera, S., Sabut, S.: Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities. Measurement 108, 55–66 (2017)CrossRefGoogle Scholar
  31. 31.
    Rai, H.M., Chatterjee, K.: A novel adaptive feature extraction for detection of cardiac arrhythmias using hybrid technique MRDWT & MPNN classifier from ECG big data. Big Data Res. 12, 13–22 (2018)CrossRefGoogle Scholar
  32. 32.
    Yang, W., Si, Y., Wang, D., Guo, B.: Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine. Comput. Biol. Med. 101, 22–32 (2018)CrossRefGoogle Scholar
  33. 33.
    Lobabi-Mirghavami, H., Abdolhossein, F.: A novel grammar-based approach to atrial fibrillation arrhythmia detection for pervasive healthcare environments. J. Comput Secur. 2, 155 (2016)Google Scholar
  34. 34.
    de Albuquerque, V.H.C., Nunes, T.M., Pereira, D.R., Luz, E.J.D.S., Menotti, D., Papa, J.P., et al.: Robust automated cardiac arrhythmia detection in ECG beat signals. Neural Comput. Appl. 29, 679–693 (2018)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbinChina

Personalised recommendations