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One Lead ECG Based Personal Identification with Feature Subspace Ensembles

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4571))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Hugo Silva

  2. Escola Superior de Tecnologia de Setúbal, Campus do IPS, Setúbal, Portugal

    Hugo Gamboa

  3. Instituto de Telecomunicações, Instituto Superior Técnico, Lisbon, Portugal

    Ana Fred

Authors
  1. Hugo Silva
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  2. Hugo Gamboa
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  3. Ana Fred
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Petra Perner

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© 2007 Springer-Verlag Berlin Heidelberg

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Silva, H., Gamboa, H., Fred, A. (2007). One Lead ECG Based Personal Identification with Feature Subspace Ensembles. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_58

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  • DOI: https://doi.org/10.1007/978-3-540-73499-4_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73498-7

  • Online ISBN: 978-3-540-73499-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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