An Enhanced Machine Learning-based Biometric Authentication System Using RR-Interval Framed Electrocardiograms
This work addresses security issues in digital health by improving ECG-based authentication, but it is incremental as it builds on existing methods with specific optimizations.
The paper tackled the problem of biometric authentication by proposing an ECG-based system that uses RR-interval framing and an Overall Performance metric, achieving up to 95% accuracy with 61 out of 70 samples accepted at an optimized upper-range control limit of 0.0028.
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%.