Electrocardiogram Beat Classification Using Support Vector Machine and Extreme Learning Machine

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 248)


The Electrocardiogram (ECG) is of significant importance in assessing patients with abnormal activity in their heart. ECG Recordings of the patient taken for analyzing the abnormality and classify what type of disorder present in the heart functionality. There are several classes of heart disorders including Premature Ventricular Contraction (PVC), Atrial Premature beat (APB), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Paced Beat (PB), and Atrial Escape Beat (AEB).To analyze ECG various feature extraction methods and classification algorithms are used. The proposed work employed discrete wavelet transform (DWT) in feature extraction on ECG signals obtained from MIT-BIH Arrhythmia Database. The Machine Learning Techniques, Support Vector Machine (SVM) and Extreme Learning Machine (ELM) have been used to classify four types of heart beats that include PVC, LBBB, RBBB and Normal. The Performance of the classifiers are analyzed and observed that ELM-Radial Basis Function Kernel taken less time to build model and out performs SVM in predictive accuracy.


Electrocardiogram Wavelet Support Vector Machine Extreme Learning Machine 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Luthra, A.: ECG Made Easy. Japee Brothers Publishers (2007)Google Scholar
  2. 2.
    Yingthawornsuk, T.: Classification of Cardiac Arrhythmia via SVM. In: 2nd International Conference on Biomedical Engineering and Technology, IPCBEE, vol. 34. IACSIT Press, Singapore (2012)Google Scholar
  3. 3.
    Jatmiko, W., Nulad, W.P., EllyMatul, I., Mursanto, P.: Heart Beat Classification Using Wavelet Feature Based on Neural Network. Wseas Transactions on Systems 10(1) (January 2011) ISSN: 1109-2777Google Scholar
  4. 4.
    KianiSarkaleh, M., Shahbahrami, A.: Classification of ECG Arrhythmias Using Discrete Wavelet Transform and Neural Networks. International Journal of Computer Science, Engineering and Applications (IJCSEA) 2(1) (February 2012)Google Scholar
  5. 5.
    Joachim’s, T., Schölkopf, B., Burges, C., Smola, A.: Making large-Scale SVM Learning Practical. In: Advances in Kernel Methods -Support Vector Learning. MIT Press, Cambridge (1999)Google Scholar
  6. 6.
    Shawe-Taylor, J., Cristianini, N.: Support Vector Machines and other kernel-based learning methods. Cambridge University Press, UK (2000)Google Scholar
  7. 7.
    Vapnik, V.N.: Statistical Learning Theory. J. Wiley & Sons, Inc., New York (1998)MATHGoogle Scholar
  8. 8.
    Koby, C., Singer, Y.: On the Algorithmic Implementation of Multi-class Kernel-based Vector Machines. Journal of Machine Learning Research 2, 265–292 (2001)Google Scholar
  9. 9.
    Siew, C.K., Huang, G.B.: Extreme Learning Machine with Randomly Assigned RBF Kernels. International Journal of Information Technology 11(1) (2005)Google Scholar
  10. 10.
    Mark, R., Moody, G.: MIT-BIH Arrhythmia Database (1997),

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer SciencePSGR Krishnammal College for WomenCoimbatoreIndia
  2. 2.GR Govindarajulu School of Applied Computer TechnologyCoimbatoreIndia

Personalised recommendations