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Neural Networks for Biomedical Signals Classification Based on Empirical Mode Decomposition and Principal Component Analysis

  • Abdoul Dalibou Abdou
  • Ndeye Fatou NgomEmail author
  • Samba Sidibé
  • Oumar Niang
  • Abdoulaye Thioune
  • Cheikh H. T. C. Ndiaye
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 204)

Abstract

The three main events presented in the electrocardiogram (ECG) signal of each heartbeat are: the P wave, the QRS complex and the T wave. Each event contains its own peak, making this important to analyze their morphology, amplitude and duration for cardiac abnormalities. In this study, we propose a system for biomedical signal analysis based on empirical mode decomposition. Mustispectral analysis is first performed to remove noise, detect QRS complex and compute the QRS wide. Then statistical features and QRS wide are after used as inputs of classifier based on neural network model. The proposed methodology is tested on real biomedical data and discussed.

Keywords

Empirical mode decomposition Neural network ECG signal classification 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Abdoul Dalibou Abdou
    • 2
  • Ndeye Fatou Ngom
    • 1
    Email author
  • Samba Sidibé
    • 1
  • Oumar Niang
    • 1
  • Abdoulaye Thioune
    • 3
  • Cheikh H. T. C. Ndiaye
    • 3
  1. 1.Laboratoire Traitement de l’Inforrmation et Systémes Intelligents (LTISI)Ecole Polytechnique de Thies (EPT)ThiesSenegal
  2. 2.Univerté de Thies, LTISI-EPTThiesSenegal
  3. 3.Univerité Cheikh Anta Diop de Dakar, LTISI-EPTDakarSenegal

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