ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks

Ayca Hanılcı; Hakan Gürkan

doi:10.38088/jise.559236

Research Article

ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks

Year 2019, , 11 - 22, 24.06.2019

Ayca Hanılcı Hakan Gürkan

https://doi.org/10.38088/jise.559236

Cited By: 6

Abstract

In this paper, an ECG
biometric identification method, based on a two-dimensional convolutional
neural network, is introduced for biometric applications. The proposed model
includes two-dimensional convolutional neural networks that work parallel and
receive two different sets of 2-dimensional features as input. First, ACDCT
features and cepstral properties are extracted from overlapping ECG signals.
Then, these features are transformed from one-dimensional representation to
two-dimensional representation by matrix manipulations. For feature learning
purposes, these two-dimensional features are given to the inputs of the
proposed model, separately. Finally, score level fusion is applied to identify
the user. Our experimental results show that the proposed biometric identification
method achieves an accuracy of %88.57 and an identification rate of 90.48% for
42 persons.

Keywords

Biometric identification; Electrocardiogram Signal; CNN

Supporting Institution

Bursa Technical University

Project Number

181N14

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