Analysis of phonocardiograph signal as a biometric application

Abstract

Heart sound is distinctive in nature. Earlier work reported that, it can also contribute a lot to recognize a person by their heart sound. A novel technique is described in this thesis for the identification and verification of the person using energy based feature set and back propagation multilayer perceptron artificial neural network classifier (BP-MLP-ANN) is used in this thesis. PCG signal is invariable, unique, universal easy to accessible and unique in nature. Heart samples were collected through ten volunteers as ten data (i.e. heart sounds) per individuals. Before feature extraction, pre-processing involves extraction of cycles, alignment, and segmentation of primary heart sound S1 and S2. This Segmentation contributes to the features extraction based on energy taken 30 windows at a time. Classification was done, using BP-MLP-ANN. 69 % of total numbers of heart sound signal were used as Training and remaining 31 % of heart sound signal were used for Testing. The identification results show 63.3824 % of performance accuracy.