  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.
