An Enhanced Electrocardiogram Biometric Authentication System Using Machine Learning

TL;DR

This paper presents an ECG-based biometric authentication system utilizing machine learning, achieving up to 92% accuracy, and investigates optimal parameters for improved performance in security and healthcare settings.

Contribution

It introduces a novel ECG-based authentication system and analyzes key parameters affecting its accuracy, providing a practical framework for secure biometric identification.

Findings

Achieved up to 92% identification accuracy.

Identified optimal ECG slicing and sampling parameters.

Provided a MATLAB toolbox for system evaluation.

Abstract

Traditional authentication systems use alphanumeric or graphical passwords, or token-based techniques that require "something you know and something you have". The disadvantages of these systems include the risks of forgetfulness, loss, and theft. To address these shortcomings, biometric authentication is rapidly replacing traditional authentication methods and is becoming a part of everyday life. The electrocardiogram (ECG) is one of the most recent traits considered for biometric purposes. In this work we describe an ECG-based authentication system suitable for security checks and hospital environments. The proposed system will help investigators studying ECG-based biometric authentication techniques to define dataset boundaries and to acquire high-quality training data. We evaluated the performance of the proposed system and found that it could achieve up to the 92 percent identification accuracy. In addition, by applying the Amang ECG (amgecg) toolbox within MATLAB, we investigated the two parameters that directly affect the accuracy of authentication: the ECG slicing time (sliding window) and the sampling time period, and found their optimal values.