CardioPRINT: Biometric identification based on the individual characteristics derived from the cardiogram

Objective: This paper investigates the potential of cardiogram-derived traits from electrocardiogram (ECG) and impedance cardiogram (ICG) for biometric identification. Additionally, the influence of induced emotions on cardiogram attributes and their impact on identification accuracy is explored. Method: We compare 7 machine learning classifiers using a dataset gathered from 202 individuals to identify the highest-performing classifiers. Subsequently, we analyze three different feature sets employing (ECG-only, ICG-only, and both ECG and ICG). Additionally, we investigate the performance of classifiers under altered emotional states to assess classifiers’ robustness. Results: The analysis demonstrates that models employed with both ECG and ICG have the highest statistically significant accuracies. The best-performing Random Forest (RF) model using both ECG and ICG achieves an average accuracy of 97.2 %. All models reveal a decrease in classification accuracies (∼13 %) when not trained and tested under identical emotional conditions. Conclusion: Our findings suggest that integration of ECG and ICG-based features could increase the accuracy of identification compared to a single-signal-based approach. Although certain models show slight robustness to altered emotional states, the effect of the emotion is evident and future selection of cardiogram-based features, as well as biometric models, should consider emotional responses.