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IJMLC 2020 Vol.10(2): 259-264 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.929

ECG Biometrics Using Spectrograms and Deep Neural Networks

Nuno Bento, David Belo, and Hugo Gamboa

Abstract—The Electrocardiogram (ECG) is considered as a physiological signature and has previously been used for biometric purposes. The contamination of the signal due to noise adds undesired intra-variability in the ECG signals, creating the need for more robust biometric systems (BSs). With the increase of interest in the application of Deep Neural Networks (DNN) to the medical field, new solutions are also being explored in the identification and authentication of individuals. The proposed architecture exploits the potential of Convolutional Neural Networks (CNN) to identify healthy subjects using temporal frequency analysis, i.e. spectrograms.

Index Terms—Biometrics, electrocardiogram, deep learning, convolutional neural networks, spectrogram.

All authors are with LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (e-mail: n.bento@campus.fct.unl.pt, {dj.belo, hgamboa}@fct.unl.pt).

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Cite: Nuno Bento, David Belo, and Hugo Gamboa, "ECG Biometrics Using Spectrograms and Deep Neural Networks," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 259-264, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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