ECG modelling using wavelet networks: application to biometrics

  • Authors:

  • Samer Chantaf;Amine Nait-Ali;Patrick Karasinski;Mohamad Khalil

  • Affiliations:

  • Laboratoire Images, Signaux et Systemes Intelligents, LiSSi, EA. 3956, Universite Paris-Est Creteil (UPEC), 61, Avenue du general de Gaulle, Creteil 94010, France.;Laboratoire Images, Signaux et Systemes Intelligents, LiSSi, EA. 3956, Universite Paris-Est Creteil (UPEC), 61, Avenue du general de Gaulle, Creteil 94010, France.;Laboratoire Images, Signaux et Systemes Intelligents, LiSSi, EA. 3956, Universite Paris-Est Creteil (UPEC), 61, Avenue du general de Gaulle, Creteil 94010, France.;Azm Center for Research in Biotechnology and its Applications, Doctoral School for Sciences and Technology, Lebanese University, El Mitein Street, B.P 210, Tripoli, Lebanon

  • Venue:
  • International Journal of Biometrics
  • Year:
  • 2010

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Abstract

This paper deals with human identification using normal ECGs. Precisely, we would like to highlight how one can achieve human identification by considering only the most significant parameters extracted from a model. In this work, parameters are extracted by modelling the ECG using wavelet networks. The radial basis neural network method is then used to classify these parameters. Thus, a useful analysis is performed to evaluate the robustness of the identification. For each recording condition, the proposed technique has been evaluated on a set of ECG signals corresponding to normal subjects. Consequently, very encouraging results have been obtained.