ECG as a biometric for individual's identification

In this paper, we investigate a new method to analyze electrocardiogram (ECG) signal, extract the features, for the real time human identification using single lead human electrocardiogram. The proposed system extracts special parts of the ECG signal starting from the P wave, the QRS complex and ending with the T wave for that we used the multiresolution wavelet analysis. Different features are selected and reconstructed from both amplitude and time interval of the ECG signal. The matching decisions are evaluated on the basis of correlation coefficient between the features and the Radial Basis function network classifier is introduced for validation and comparison. The performance evaluation was carried out on four ECG public databases with a total of 149 persons subjected to different physical activities and heart conditions, the preliminary results indicate that the system achieved an accuracy of 90-93%.