Journal of Medical Systems

, Volume 36, Issue 2, pp 883–892 | Cite as

Automatic Classification of Heartbeats Using Wavelet Neural Network

  • Radhwane Benali
  • Fethi Bereksi Reguig
  • Zinedine Hadj Slimane


The electrocardiogram (ECG) signal is widely employed as one of the most important tools in clinical practice in order to assess the cardiac status of patients. The classification of the ECG into different pathologic disease categories is a complex pattern recognition task. In this paper, we propose a method for ECG heartbeat pattern recognition using wavelet neural network (WNN). To achieve this objective, an algorithm for QRS detection is first implemented, then a WNN Classifier is developed. The experimental results obtained by testing the proposed approach on ECG data from the MIT-BIH arrhythmia database demonstrate the efficiency of such an approach when compared with other methods existing in the literature.


ECG Feature extraction QRS Classification WNN Wavelet Cardiac arrhythmia 


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Radhwane Benali
    • 1
  • Fethi Bereksi Reguig
    • 1
  • Zinedine Hadj Slimane
    • 1
  1. 1.Biomedical Engineering Laboratory, Department of Electronics, Faculty of Engineering SciencesAbou Bekr Belkaid UniversityTlemcenAlgeria

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