Kernel-Based Feature Relevance Analysis for ECG Beat Classification

  • D. F. Collazos-HuertasEmail author
  • A. M. Álvarez-Meza
  • N. Gaviria-Gómez
  • G. Castellanos-Dominguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9117)


The analysis of Electrocardiogram (ECG) records for arrhythmia classification favors the developing of aid diagnosis systems. However, current devices provide large amounts of data being necessary the development of signal processing methodologies to reveal relevant information. Here, a kernel-based feature relevance analysis approach is introduced to highlight discriminative attributes in ECG-based arrhythmia classification tasks. For such purpose, morphological and spectral-based features are extracted from each provided heartbeat. Then, a linear mapping is learned by using a Kernel Centered Alignment-based scheme to highlight the most relevant features when estimating nonlinear dependencies among samples. The proposed approach is performed as a cascade classification scheme to avoid biased results due to unbalance issue of the studied phenomenon. The results yield a performance rate of \(86.52\,\%\) (sensitivity), \(97.57\,\%\) (specificity), and \(92.57\,\%\) (accuracy) in a well-known database, which validate the reliability of the proposed algorithm in comparison to the state-of-art.


Kernel functions ECG feature extraction Relevance analysis 



This work is supported by Programa Nacional de Formación de Investigadores “Generación del Bicentenario”, 2011/2012 funded by COLCIENCIAS and the project “Plataforma tecnológica para los servicios de teleasistencia, emergencias médicas, seguimiento y monitoreo permanente de pacientes y apoyo a los programas de prevención” Eje 3 - ARTICA.


  1. 1.
    Kutlu, Y., Kuntalp, D.: Feature extraction for ecg heartbeats using higher order statistics of wpd coefficients. Comput. Methods Programs Biomed. 105(3), 257–267 (2012)CrossRefGoogle Scholar
  2. 2.
    Karpagachelvi, S., Arthanari, M., Sivakumar, M.: Ecg feature extraction techniques-a survey approach. arXiv preprint arXiv:1005.0957 (2010)
  3. 3.
    da S. Luz, E.J., Nunes, T.M., De Albuquerque, V.H.C., Papa, J.P., Menotti, D.: Ecg arrhythmia classification based on optimum-path forest. Expert Syst. Appl. 40(9), 3561–3573 (2013)CrossRefGoogle Scholar
  4. 4.
    Wang, J.S., Chiang, W.C., Hsu, Y.L., Yang, Y.T.C.: Ecg arrhythmia classification using a probabilistic neural network with a feature reduction method. Neurocomputing 116, 38–45 (2013)CrossRefGoogle Scholar
  5. 5.
    Yeh, Y.C., Chiou, C.W., Lin, H.J.: Analyzing ecg for cardiac arrhythmia using cluster analysis. Expert Syst. Appl. 39(1), 1000–1010 (2012)CrossRefGoogle Scholar
  6. 6.
    Nejadgholi, I., Moradi, M.H., Abdolali, F.: Using phase space reconstruction for patient independent heartbeat classification in comparison with some benchmark methods. Comput. Biol. Med. 41(6), 411–419 (2011)CrossRefGoogle Scholar
  7. 7.
    Zhang, L., Peng, H., Yu, C.: An approach for ecg classification based on wavelet feature extraction and decision tree. In: 2010 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–4. IEEE (2010)Google Scholar
  8. 8.
    Brockmeier, A., Choi, J., Kriminger, E., Francis, J., Principe, J.: Neural decoding with kernel-based metric learning. Neural Comput. 26, 1080–1107 (2014)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Rodríguez-Sotelo, J.L., Peluffo-Ordoñez, D., Cuesta-Frau, D., Castellanos-Domínguez, G.: Unsupervised feature relevance analysis applied to improve ecg heartbeat clustering. Comput. Methods Programs Biomed. 108(1), 250–261 (2012)CrossRefGoogle Scholar
  10. 10.
    Castro Hoyos, C., Peluffo Ordonez, D.H., Rodríguez-Sotelo, J.L., Dominguez, G.C.: Effectiveness of morphological and spectral heartbeat characterization on arrhythmia clustering for holter recordings. In: SIPAIM (2014)Google Scholar
  11. 11.
    Cortes, C., Mohri, M., Rostamizadeh, A.: Algorithms for learning kernels based on centered alignment. J. Mach. Learn. Res. 13, 795–828 (2012)zbMATHMathSciNetGoogle Scholar
  12. 12.
    Álvarez-Meza, A.M., Cárdenas-Peña, D., Castellanos-Dominguez, G.: Unsupervised kernel function building using maximization of information potential variability. In: Bayro-Corrochano, E., Hancock, E. (eds.) CIARP 2014. LNCS, vol. 8827, pp. 335–342. Springer, Heidelberg (2014) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • D. F. Collazos-Huertas
    • 1
    Email author
  • A. M. Álvarez-Meza
    • 1
  • N. Gaviria-Gómez
    • 2
  • G. Castellanos-Dominguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaManizalesColombia
  2. 2.Grupo Telecomunicaciones Aplicadas (GITA)Universidad de AntioquiaMedellínColombia

Personalised recommendations