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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)

Abstract

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.

Keywords

Kernel functions ECG feature extraction Relevance analysis 

Notes

Acknowledgments

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.

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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

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