Biometric system from heart sound using wavelet based feature set

Heart sound is generally used to determine the human heart condition. Recent reported research proved that cardiac auscultation technique which uses the characteristics of phonocardiogram (PCG) signal, can be used as biometric authentication system. An automatic method for person identification and Verification from PCG using wavelet based feature set and Back Propagation Multilayer Perceptron Artificial Neural Network (BP-MLP-ANN) classifier is presented in this paper. The work proposes a time frequency domain novel feature set based on Daubechies wavelet with second level decomposition. Time-frequency domain information is obtained from wavelet transform which in turn is reflected in wavelet based feature set which carries important information for biometric identification. Database is collected from 10 volunteers (between 20-40 age groups) during one month period using a digital stethoscope manufactured by HDfono Doc. The proposed algorithm is tested on 4000 PCG samples and yields 90.52% of identification accuracy and Equal Error Rate (EER) of 9.48% The preprocessing before feature extraction involves selection of heart cycle, filtering for noise reduction, aligning and segmentation of Si and S2. Performance of the classifier is determined from the Receiver operating curve (ROC). The experimental result shows that the performance of the proposed algorithm is better than the other reported technique which uses Linear Band Frequency Cepstral coefficient (LBFCC) feature set.