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Signal, Image and Video Processing

, Volume 8, Issue 5, pp 931–942 | Cite as

Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods

  • Naif Alajlan
  • Yakoub Bazi
  • Farid Melgani
  • Salim Malek
  • Mohamed A. Bencherif
Original Paper

Abstract

In this paper, we propose to investigate the capabilities of two kernel methods for the detection and classification of premature ventricular contractions (PVC) arrhythmias in Electrocardiogram (ECG signals). These kernel methods are the support vector machine and Gaussian process (GP). We propose to study these two classifiers with various feature representations of ECG signals, such as morphology, discrete wavelet transform, higher-order statistics, and S transform. The experimental results obtained on 48 records (i.e., 109,887 beats) of the MIT-BIH Arrhythmia database showed that for all feature representation adopted in this work, the GP detector trained only with 600 beats from PVC and Non-PVC classes can provide an overall accuracy and a sensitivity above 90 % on 20 records (i.e., 49,774 beats) and 28 records (i.e., 60,113 beats) seen and unseen, respectively, during the training phase.

Keywords

Premature ventricular contraction (PVC) Support vector machines (SVMs) Gaussian process classifiers (GPCs) Morphology Wavelet transform High-order statistics S transform 

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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Naif Alajlan
    • 1
  • Yakoub Bazi
    • 1
  • Farid Melgani
    • 2
  • Salim Malek
    • 1
  • Mohamed A. Bencherif
    • 1
  1. 1.ALISR Laboratory, College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia
  2. 2.Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly

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