ElectroCardioGram (ECG)-based user authentication using deep learning algorithms
Date
2023
Authors
Agrawal, Vibhav
Hazratifard, Mehdi
Elmiligi, Haytham
Gebali, Fayez
Journal Title
Journal ISSN
Volume Title
Publisher
Diagnostics
Abstract
Personal authentication security is an essential area of research in privacy and cybersecurity.
For individual verification, fingerprint and facial recognition have proved particularly useful. However,
such technologies have flaws such as fingerprint fabrication and external impediments. Different
AI-based technologies have been proposed to overcome forging or impersonating authentication
concerns. Electrocardiogram (ECG)-based user authentication has recently attracted considerable
curiosity from researchers. The Electrocardiogram is among the most reliable advanced techniques
for authentication since, unlike other biometrics, it confirms that the individual is real and alive. This
study utilizes a user authentication system based on electrocardiography (ECG) signals using deep
learning algorithms. The ECG data are collected from users to create a unique biometric profile for
each individual. The proposed methodology utilizes Convolutional Neural Networks (CNNs) and
Long Short-Term Memory (LSTM) to analyze the ECG data. The CNNs are trained to extract features
from the ECG data, while the LSTM networks are used to model the temporal dependencies in the
data. The evaluation of the performance of the proposed system is conducted through experiments. It
demonstrates that it effectively identifies users based on their ECG data, achieving high accuracy rates.
The suggested techniques obtained an overall accuracy of 98.34% for CNN and 99.69% for LSTM
using the Physikalisch–Technische Bundesanstalt (PTB) database. Overall, the proposed system
offers a secure and convenient method for user authentication using ECG data and deep learning
algorithms. The approach has the potential to provide a secure and convenient method for user
authentication in various applications.
Description
Keywords
user authentication, ElectroCardioGram (ECG), deep learning algorithms, LSTM and PTB database, telehealth system, CNN and LSTM training and validation
Citation
Agrawal, V., Hazratifard, M., Elmiligi, H., & Gebali, F. (2023). “ElectroCardioGram (ECG)-based user authentication using deep learning algorithms.” Diagnostics, 13(3), 439. https://doi.org/10.3390/diagnostics13030439