One-lead ECG for identity verification

TLDR

The shapes of ECG waveforms differ between individuals, yet it is unclear whether these differences can reliably identify people, and a one‑lead ECG is a low‑frequency, one‑dimensional signal recorded from hand electrodes. This research investigates the feasibility of using the electrocardiogram (ECG) as a new biometric for human identity verification. The study applied template matching and a decision‑based neural network (DBNN) to implement identity verification. The results showed that a one‑lead ECG can identify individuals with 95 % accuracy using template matching, 80 % with a DBNN, and 100 % when the two methods are combined, demonstrating its potential as a biometric.

Abstract

This research investigates the feasibility of using the electrocardiogram (ECG) as a new biometric for human identity verification. It is well known that the shapes of the ECG waveforms of different persons are different but it is unclear whether such differences can be used to identify different individuals. In this research, we demonstrated successfully that it is possible to identify a specific person from a group of candidates using a one-lead ECG. A one-lead ECG, unlike two-dimensional biometrics, such as the fingerprint, is a one-dimensional, low-frequency signal that can be recorded from electrodes on the hands. This research applied two techniques, template matching and a decision-based neural network (DBNN), to implement the identity verification. Using each of the two methods separately on a predetermined group of 20 subjects, the experimental results showed that the rate of correct identity verification was 95% for template matching and 80% for the DBNN. Combining the two methods produced a 100% correct rate. Our results show that ECG analysis is a potentially applicable method for human identity verification.