One-Lead ECG-based Personal Identification Using Ziv-Merhav Cross Parsing

The advance of falsification technology increases security concerns and gives biometrics an important role in security solutions. The electrocardiogram (ECG) is an emerging biometric that does not need liveliness verification. There is strong evidence that ECG signals contain sufficient discriminative information to allow the identification of individuals from a large population. Most approaches rely on ECG data and the fiducia of different parts of the heartbeat waveform. However non-fiducial approaches have proved recently to be also effective, and have the advantage of not relying critically on the accurate extraction of fiducia data. In this paper, we propose a new non-fiducial ECG biometric identification method based on data compression techniques, namely the Ziv-Merhav cross parsing algorithm for symbol sequences (strings). Our method relies on a string similarity measure derived from algorithmic cross complexity concept and its compression-based approximation. We present results on real data, one-lead ECG, acquired during a concentration task, from 19 healthy individuals. Our approach achieves 100% subject recognition rate despite the existence of differentiated stress states.