An Enhanced Machine Learning-Based Biometric Authentication System Using RR-Interval Framed Electrocardiograms

Song-Kyoo Kim,Chan Yeob Yeun,Paul D Yoo

doi:10.1109/access.2019.2954576

Abstract

This paper is targeted in the area of biometric data enabled security system based on the machine learning for the digital health. The disadvantages of traditional authentication systems include the risks of forgetfulness, loss, and theft. Biometric authentication is therefore rapidly replacing traditional authentication methods and is becoming an everyday part of life. The electrocardiogram (ECG) was recently introduced as a biometric authentication system suitable for security checks. The proposed authentication system helps investigators studying ECG-based biometric authentication techniques to reshape input data by slicing based on the RR-interval, and defines the Overall Performance (OP), which is the combined performance metric of multiple authentication measures. We evaluated the performance of the proposed system using a confusion matrix and achieved up to 95% accuracy by compact data analysis. We also used the Amang ECG (amgecg) toolbox in MATLAB to investigate the upper-range control limit (UCL) based on the mean square error, which directly affects three authentication performance metrics: the accuracy, the number of accepted samples, and the OP. Using this approach, we found that the OP can be optimized by using a UCL of 0.0028, which indicates 61 accepted samples out of 70 and ensures that the proposed authentication system achieves an accuracy of 95%.

Highlights

Recent new technologies in the mixed area between big data and artificial intelligence are changing the way of healthcare provision which could give the huge impact on the industry
This paper is targeted in the area of biometric data enabled security system based on the machine learning for the digital health
SEPUP OF EXPERIMENTS: SECURITY CHECK CASE Several machine learning (ML)-based approaches have been tested to develop proper regression models, but our studies have indicated that the decision tree (DT) method provides the best performance with RR-interval framing (RRIF) ECG data [16]

Summary

INTRODUCTION

Recent new technologies in the mixed area between big data and artificial intelligence are changing the way of healthcare provision which could give the huge impact on the industry. We have updated the extended functions and demonstrations using the Amang ECG (amgecg) toolbox [19] for RRIF to generate the input for the regression approach during the training and testing phases. This rest of the article is divided into three sections.

ELECTROCARDIOGRAM BASED AUTHENTICATION USING MACHINELEARNING

SEPUP OF EXPERIMENTS

NEW PERFORMANCE MEASURE

MSE BASED UCL DEPENDENCIES

Findings

CONCLUSION