AN ENHANCED MODEL FOR USING ELECTROCARDIOGRAM (ECG) SIGNALS AS HUMAN BIOMETRIC

Abstract

Biometrics is an interesting study due to the amazing progress in security technology and defined as a method of recognizing humans based on a physiological characteristics (as face, fingerprints, DNA, ECG, etc…) or behavioral characteristics( as voice, gait, keystroke, signature, etc…). The term biometrics comes from the Greek words 'bio' (life) and 'metrics' (measurement), so biometrics means life measurement. Electrocardiogram (ECG) signal analysis is an active research area for diagnoses which is a method to measure the change in electrical potential of the heart over time. This work investigates in ECG signals as a biometric trait which based on uniqueness represented by physiological and geometrical of ECG signal. Biometric systems based ECG classified into two categories fiducial and non-fiducial approaches depend on the feature extraction methods.

In this work, a proposed non-fiducial identification system is presented with a comparative study using Radial Basis Function (RBF) neural network, Back Propagation (BP) neural network and Support Vector Machine (SVM) as classification methods. The Discrete Wavelet Transform method is applied to