The ECGXtractor deep learning system achieved Equal Error Rates of 0.14% and 2.06% in single- and multi-session ECG biometric verification using the PTB database.
The proposed Deep Learning technology, ECGXtractor, demonstrates high accuracy for ECG-based biometric recognition across single- and multi-session scenarios.
ECGs have shown unique patterns to distinguish between different subjects and present important advantages compared to other biometric traits. However, the lack of public data and standard experimental protocols makes the evaluation and comparison of novel ECG methods difficult. In this study, we perform extensive analysis and comparison of different scenarios in ECG biometric recognition. We consider verification and identification tasks, single- and multi-session settings, and single- and multi-lead ECGs recorded with traditional and user-friendly devices. We also present ECGXtractor, a robust Deep Learning technology trained with an in-house large-scale database, and evaluate it with detailed experimental protocol and public databases. With the popular PTB database, we achieve Equal Error Rates of 0.14% and 2.06% in single- and multi-session verification. The results achieved prove the soundness of ECGXtractor across multiple scenarios and databases. We release the source code, experimental protocol details, and pre-trained models in GitHub to advance in the field.
Melzi et al. (2023) studied Biometric recognition (n=122,622). ECGXtractor was evaluated on Equal Error Rate (EER) in multi-session verification (PTB database). The ECGXtractor deep learning system achieved Equal Error Rates of 0.14% and 2.06% in single- and multi-session ECG biometric verification using the PTB database.