Paper Info
Title
EDITH : ECG Biometrics Aided by Deep Learning for Reliable Individual Authentication
Abstract
In recent years, physiological signal-based authentication has shown great promises, for its inherent robustness against forgery. Electrocardiogram (ECG) signal, being the most widely studied biosignal, has also received the highest level of attention in this regard. It has been proven with numerous studies that by analyzing ECG signals from different persons, it is possible to identify them, with acceptable accuracy. In this work, we present, EDITH, a deep learning-based framework for ECG biometrics authentication system. Moreover, we hypothesize and demonstrate that Siamese architectures can be used over typical distance metrics for improved performance. We have evaluated EDITH using 4 commonly used datasets and outperformed the prior works using a fewer number of beats. EDITH performs competitively using just a single heartbeat (96 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 99.75% accuracy) and can be further enhanced by fusing multiple beats (100% accuracy from 3 to 6 beats). Furthermore, the proposed Siamese architecture manages to reduce the identity verification Equal Error Rate (EER) to 1.29 %. A limited case study of EDITH with real-world experimental data also suggests its potential as a practical authentication system.
Year
DOI
Venue
2022
10.1109/TETCI.2021.3131374
IEEE Transactions on Emerging Topics in Computational Intelligence
Keywords
DocType
Volume
Authentication,biometrics,ECG-ID,1D convolutional network,1D Siamese network
Journal
6
Issue
ISSN
Citations 
4
2471-285X
1
PageRank 
References 
Authors
0.35
24
8
Name
Order
Citations
PageRank
Nabil Ibtehaz110.35
Muhammad Chowdhury24414.54
Amith Khandakar32412.00
Serkan Kiranyaz475061.15
M. Sohel Rahman548856.99
Anas Tahir611.03
Yazan Qiblawey710.69
Tawsifur Rahman810.69