Bio-metric authentication with electrocardiogram (ECG) by considering variable signals

The use of conventional bio-signals such as an electrocardiogram (ECG) for biometric authentication is vulnerable to a lack of verification of continuity of signals; this is because the system does not consider the change in signals caused by a change in the situation of a person, that is, conventional biological signals. Prediction technology based on tracking and analyzing new signals can overcome this shortcoming. However, since the biological signal data sets are massive, their utilization is crucial for higher accuracy. In this study, we defined a 10 $ \times $ 10 matrix for 100 points based on the R-peak point and an array for the dimension of the signals. Furthermore, we defined the future predicted signals by analyzing the continuous points in each array of the matrices at the same point. As a result, the accuracy of user authentication was 91%.