One Lead ECG Based Personal Identification with Feature Subspace Ensembles

  • Hugo Silva
  • Hugo Gamboa
  • Ana Fred
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4571)


In this paper we present results on real data, focusing on personal identification based on one lead ECG, using a reduced number of heartbeat waveforms. A wide range of features can be used to characterize the ECG signal trace with application to personal identification. We apply feature selection (FS) to the problem with the dual purpose of improving the recognition rate and reducing data dimensionality. A feature subspace ensemble method (FSE) is described which uses an association between FS and parallel classifier combination techniques to overcome some FS difficulties. With this approach, the discriminative information provided by multiple feature subspaces, determined by means of FS, contributes to the global classification system decision leading to improved classification performance. Furthermore, by considering more than one heartbeat waveform in the decision process through sequential classifier combination, higher recognition rates were obtained.


Feature Selection Recognition Rate Feature Selection Method Feature Selection Algorithm Combination Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hugo Silva
    • 1
  • Hugo Gamboa
    • 2
  • Ana Fred
    • 3
  1. 1.Instituto de Telecomunicações, LisbonPortugal
  2. 2.Escola Superior de Tecnologia de Setúbal, Campus do IPS, SetúbalPortugal
  3. 3.Instituto de Telecomunicações, Instituto Superior Técnico, LisbonPortugal

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