ECG analysis for person identification

Abstract

Electrocardiogram (ECG) has been actively proposed as aliveness biometric. In this paper, the study which concern to a realistic application is proposed. Firstly, a single lead normal ECG signal is acquired from individuals of 10 subjects. Then, each single beat ECG is segmented and analyzed in Continuous Wavelet Transform (CWT) domain. Total energy of wavelet coefficients for each P, QRS, and T segment is calculated. Next, the Fisher Linear Discriminant Analysis (FLDA) is applied. Finally, normalized Euclidean distance is implemented as a classifier. In experimental results, 97% of classification accuracy is achieved in case of a normal ECG (with non-variation of heart rate). © 2013 IEEE.