Compression-Based ECG Biometric Identification Using a Non-fiducial Approach

TL;DR

This paper introduces a novel compression-based non-fiducial ECG biometric identification method using extended-alphabet finite-context models and normalized relative compression, achieving state-of-the-art results on a benchmark database.

Contribution

It presents a new non-fiducial approach utilizing compression models and extended-alphabet finite-context models for ECG biometric identification, improving accuracy over existing methods.

Findings

Achieved state-of-the-art results on a benchmark ECG database.

Utilized normalized relative compression as a similarity measure.

Demonstrated effectiveness of extended-alphabet finite-context models on quantized derivatives.

Abstract

Due to its characteristics, there is a trend in biometrics to use the ECG signal for personal identification. Recent works based on compression models have shown that these approaches are suitable to ECG biometric identification. However, the best results are usually achieved by the methods that, at least, rely on one point of interest of the ECG. In this work, we propose a compression-based non-fiducial method, that uses a measure of similarity, called the Normalized Relative Compression -- a measure related to the Kolmogorov complexity of strings. Our method uses extended-alphabet finite-context models (xaFCMs) on the quantized first-order derivative of the signal, instead of using directly the original signal, as other methods do. We were able to achieve state-of-the-art results on a database collected at the University of Aveiro, which was used on previous works, making it a good preliminary benchmark for the method.