Robustness study of ECG biometric identification in heart rate variability conditions

Abstract

Electrocardiogram (ECG) has been actively proposed as an aliveness biometric for years. In realistic application, this type of biometrics still needs to be verified in some conditions related to the practical use. Therefore, we propose in this paper the study of two themes that concern its realistic application. First, the single beat of an electrocardiogram (SB-ECG) is used as the ECG feature, which aims to decrease the time to acquire the ECG from the individual. Second, for the robustness study of ECG features to heart rate variability (HRV), we investigate the relation between the correct identification and the HRV conditions. The HRV-ECG data is set up and collected by using the Bio-Pac system. The overall processes of the proposed method are the following: Continuous wavelet transform (CWT) is used to analyze each SB-ECG. Then the root-mean square (RMS) value of total energy of the wavelet coefficients of each P, QRS, and T segment is calculated. Next, the Fisher linear discriminant analysis (FLDA) is applied to all sets of the RMS value for dimension reduction. Lastly, the normalized Euclidean distance is implemented as the classifier. The experimental results demonstrate that, with the proposed method, the classification achieves 97% accuracy and the robustness study achieves over 80% accuracy with HRV-ECG change up to 20%. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.