A Study on Electrocardiogram based Biometrics using Embedded Module

Biometrics technology uses bio-signal data, which are unique for each person, as features for identification. Among the biometrics, electrocardiogram (ECG) signals, which are related to the heartbeat, can be used for personal identification as well as disease diagnosis, and also makes it easier to miniaturize measuring devices compared to other bio-signals. In this paper, an ECG-based personal identification system using embedded module is proposed. When an ECG signal is entered, the computer removes noise and segments the signal, after which the signals are transmitted to the embedded module. The embedded module extracts the fiducial point features of the ECG signal and classifies ECG data. Experiment results showed that the segmented drive and the single drive exhibited equal results, and the equal error rate (EER) was the lowest at an average of 0.74% when test data of 6 cycles. To shorten the operating time of the implemented personal identification system, three embedded module optimization methods were used, it decreased by 66.1%. Thereby confirming potential use of the identification system by using ECG signals based small devices.