ECG biometric authentication using a dynamical model

Abstract

This paper concerns the authentication of individuals through analysis of electrocardiogram (ECG) signals. Because the human heart differs physiologically from one person to the next, ECG signals represent a rich source of information that offers strong potential for authentication or identification. We describe a novel approach to ECG-based biometrics in which a dynamical-systems model is employed, resulting in improved registration of pulses as compared to previous techniques. Parameters at the fiducial points are detected using a sum-of-Gaussians representation, resulting in an 18-component feature vector that can be used for classification. Using a publicly available dataset of ECG signals from 47 participants, a classifier was formulated using quadratic discriminant analysis (QDA). The observed mean authentication accuracies were 90% and 97% using 100 beats and 300 beats, respectively. Although tested with standard ECG signals only, we believe that the approach can be extended to other sensor types, such as fingertip-ECG devices.