Identity Recognition Based on the QRS Complex Dynamics of Electrocardiogram

Biometric identification based on electrocardiogram signals has attracted increasing attention. As the most dominant feature of the electrocardiogram signal, the QRS complex(i.e., the combination of ECG Q, R, and S waves) has been used for identification in some studies. This study aims to investigate the intra-individual stability of the QRS complex dynamics to assess its potential for human identification. The QRS complex dynamics are used as the unique feature to classify the QRS complex, which differs from the time/frequency domain features used in the literature. It is the fundamental feature of the QRS complex and contains the underlying information of the QRS complex. The dynamics of training QRS complexes are extracted and expressed as a constant radial basis function network by using deterministic learning. A set of estimators is constructed to represent the training QRS complexes using constant radial basis function networks. By comparing this set of estimators with the test QRS complex, a set of recognition errors is generated, and the average L
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norms of the errors are taken as the similarity measure between the dynamics of the training QRS complexes and that of the test QRS complex. Therefore, the test QRS complex can be recognized according to the smallest error principle. The electrocardiogram is classified according to the vote of the test QRS complexes recognition results. A private database and PTB diagnostic ECG database are used to test the proposed method. Experimental results on the private database (PTB database) showed that the average identification accuracy was 96.12% (97.42%) for 5-fold cross-validation based on one-lead electrocardiogram and 99.50% (99.23%) for 2-fold cross-validation based on two-lead electrocardiogram, respectively. These show that the dynamics of the QRS complex are well-differentiated for different individuals.