Compression-Based Classification of ECG Using First-Order Derivatives

Abstract

Due to its characteristics, there is a trend in biometrics to use the ECG signal for personal identification. There are different applications for this, namely, adapting entertainment systems to personal settings automatically. 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 – called fiducial methods. 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.