Fiducial ECG-Based Biometry: Comparison of Classifiers and Dimensionality Reduction Methods

Abstract

Biometry is becoming increasingly important in order to identify or authenticate individuals. Since the seminar work of Biel et at. in 1999 and 2001, the feasibility of using the electrocardiogram (ECG) for biometric recognition has been considered by several authors. Both fiducial methods, which are based on using fiducial points related to the detected QRS complexes, and non-fiducial methods, which do not require the extraction of the QRS complexes from the signals, have been considered. However, the feasibility of ECG-based biometry is still unclear, as the results from different studies are difficult to compare. In this paper, we concentrate on fiducial methods, comparing the performance of several classifiers and dimensionality reduction techniques on a publicly available dataset. Our results show that ECG-based biometry is indeed a feasible alternative to other widely used biometric traits, since an accuracy above 99.95% can be attained with the appropriate choice of the dimensionality reduction method and classifier.