A novel feature set for deployment in ECG based biometrics

In the last two decades, the Electrocardiogram (ECG) was introduced as a powerful biometric tool for personal identification. The vast majority of publications in the ECG based biometrics domain have focused on extracting fiducial based features for use in the identification task. Fiducial based features refer to the landscape of an ECG, which encompasses three complex waves for each heartbeat. The fiducial based approach requires calculating amplitude and temporal distances between 11 fiducial points that represent the peaks, valleys, onsets and offsets of these waves. The purpose of this research is to investigate the efficiency of a subset of 23 fiducial features that has the advantage of relaxing the detection process to include only five points that represent the peaks and valleys of the three complexes. For comparison, a super set of 36 fiducial features and the subset of 23 features were examined using radial basis functions (RBF) neural network classifier. A dataset of 35 records of 13 subjects from PTB Physionet database was used for training and testing purposes. Thereafter, the generalization ability of the system to other datasets was tested using another set of 38 subjects from PTB database. The results show the ability of the proposed subset of 23 features to maintain the identification accuracy and provide better generalization results than the super set.