ECG biometric recognition in different physiological conditions using robust normalized QRS complexes

This paper demonstrates subject recognition using electrocardiogram (ECG) signal in different physiological conditions. A total of 30 subjects used in this study were obtained from a non-invasive measurement called the Revitus ECG module. Each subject performed six physiological activities which are walking, going upstairs, going downstairs, natural gait, lying with position changed and resting while watching TV. Unique features were extracted in these different physiological conditions from the same subject using normalized QRS complex technique. One physiological activity acts as the enrolment template while the remaining five activities represent the recognition data. Cross correlation was used to measure the similarity between activities. Later, Multilayer Perceptron classifier was applied to evaluate the distinctiveness between subjects. The results of the experiment show that QRS complexes in different activities from the same subject were strongly correlated to each other by obtaining correlation values of more than 0.9. A classification accuracy of 96.1% when using the proposed normalized method as compared to 93.4% without using the normalized QRS complex proves to distinguish between subjects.