(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Computer Research (IJACR)

ISSN (Print):2249-7277    ISSN (Online):2277-7970
Volume-10 Issue-47 March-2020
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Paper Title : ECG signals for human identification based on fiducial and non-fiducial approaches
Author Name : Anwar E. Ibrahim, Salah Abdel- Mageid, Nadra Nada and Marwa A. Elshahed
Abstract :

Biometric systems are mostly used for human identification and authentication. Recent developments have shown that ECG human identification can be used as a powerful tool as it gives more reliable and accurate results. In this paper, a proposed human identification system based on ECG as a biometric is presented with different feature extraction methods. Different feature extraction methods such as Daubechies wavelet ('db3', 'db8' and 'db10'), Symlets wavelet 'sym7' and Biorthogonal wavelet 'bior2.6' are exploited in this work. A combination of radial basis function (RBF) neural network and Backpropagation (BP) neural network is used as a classifier. The proposed system gives an identification rate of 98.41% with Daubechies wavelet 'db8'. In addition, the identification rate for Daubechies wavelet ('db3'and 'db10'), Symlets wavelet 'sym7' and Biorthogonal wavelet 'bior2.6' increases when R-R intervals are added as fiducial features of the non-fiducial features.

Keywords : Biometric, ECG signal, Fiducial, Non-fiducial.
Cite this article : Ibrahim AE, Mageid SA, Nada N, Elshahed MA. ECG signals for human identification based on fiducial and non-fiducial approaches. International Journal of Advanced Computer Research. 2020; 10 (47): 89-95. DOI:10.19101/IJACR.2019.940129.
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