ECG-Based User Authentication Using Deep Learning Architectures
Date
2023-03-22
Authors
Agrawal, Vibhav
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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 like 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 is 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