Bipolar Pulse Active features for ECG biometric application

This paper introduce a new Bipolar Pulse Active (BPA) feature extraction technique implemented to electrocardiograms (ECG) for biometric authentication.. The BPA extracts information from ECG signals and decomposes them, using a series of harmonically related periodic triangular waveforms, into a finite set of Pulse Domain features. In this work, BPA is used to compare the performance of ECG when the information taken from 3 different locations, namely peaks P to T, peaks P to R and peaks R to T. The authentication performance is analysed with and without the use of classifier. In this work, Linear Discriminant Analysis (LDA) is used a classifier to evaluate the BPA ECG based features for biometric authentication.