Pulse ID: The Case for Robustness of ECG as a Biometric Identifier

Electrocardiogram (ECG) signals are known to encode unique signatures based on the geometrical characteristics of the heart. Due to other advantages ?- such as continuity and accessibility (now via smartwatch technology) -- ECG could make for a robust biometric ID system. We show that single-node ECG measurements through an Apple Watch would suffice to identify an individual. Apart from the Apple Watch ECG data, we have also performed analysis on two other ECG datasets from PhysioNet to test the robustness of our methods in two situations: in particular, we tested how it holds up against high volume (across a large number of individuals) and high variability (across different states of activity). We have also compared multiple classifier models in combination with different feature sets to identify the most superior combination. We observed Equal Error Rate (EER) values that were consistently <3%. Our results show that ECG proves to be very effective and robust.