ECG biometric authentication using a dynamical model

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.