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

Ayca Hanılcı; Hakan Gürkan

doi:10.38088/jise.559236

EN

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

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

References

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Ayca Hanılcı
0000-0002-1314-3743
Türkiye

Hakan Gürkan ^*
0000-0002-7008-4778
Türkiye

Publication Date

June 24, 2019

Submission Date

April 30, 2019

Acceptance Date

May 29, 2019

Published in Issue

Year 1970 Volume: 3 Number: 1

DOI

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

IZ

https://izlik.org/JA96GW89NM

Cite

RIS / Bibtex

APA

Hanılcı, A., & Gürkan, H. (2019). ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks. Journal of Innovative Science and Engineering, 3(1), 11-22. https://doi.org/10.38088/jise.559236

AMA

1.Hanılcı A, Gürkan H. ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks. JISE. 2019;3(1):11-22. doi:10.38088/jise.559236

Chicago

Hanılcı, Ayca, and Hakan Gürkan. 2019. “ECG Biometric Identification Method Based on Parallel 2-D Convolutional Neural Networks”. Journal of Innovative Science and Engineering 3 (1): 11-22. https://doi.org/10.38088/jise.559236.

EndNote

Hanılcı A, Gürkan H (June 1, 2019) ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks. Journal of Innovative Science and Engineering 3 1 11–22.

IEEE

[1]A. Hanılcı and H. Gürkan, “ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks”, JISE, vol. 3, no. 1, pp. 11–22, June 2019, doi: 10.38088/jise.559236.

ISNAD

Hanılcı, Ayca - Gürkan, Hakan. “ECG Biometric Identification Method Based on Parallel 2-D Convolutional Neural Networks”. Journal of Innovative Science and Engineering 3/1 (June 1, 2019): 11-22. https://doi.org/10.38088/jise.559236.

JAMA

1.Hanılcı A, Gürkan H. ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks. JISE. 2019;3:11–22.

MLA

Hanılcı, Ayca, and Hakan Gürkan. “ECG Biometric Identification Method Based on Parallel 2-D Convolutional Neural Networks”. Journal of Innovative Science and Engineering, vol. 3, no. 1, June 2019, pp. 11-22, doi:10.38088/jise.559236.

Vancouver

1.Ayca Hanılcı, Hakan Gürkan. ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks. JISE. 2019 Jun. 1;3(1):11-22. doi:10.38088/jise.559236