Utilization of a hierarchical electrocardiogram classification model for enhanced biometric identification

Emerging research on artificial intelligence (AI) has leveraged the unique properties of electrocardiogram (ECG) signals for user identification. ECG signals, known for their resistance to forgery and tampering, offer security advantages. However, these signals fluctuate in response to physical and cognitive stress. Despite their security benefits, these dynamic characteristics present challenges for consistent user identification owing to their variable amplitudes and shapes. To address these problems, we propose a 2-stage user identification system that integrates ECG signals and status information. This system classifies the user’s ECG status and uses the feature values in a second model to improve dynamic feature learning ability. This allows identification with high accuracy even in various stress states of the user. This increases the real-life usability of the ECG user identification system. The effectiveness of the proposed method was confirmed through a performance evaluation using CSU-BIODB(Chosun University-BIO Database) and the public MIT-BIH(Massachusetts Institute of Technology - Beth Israel Hospital Arrhythmia Laboratory) ST Change database, with identification accuracies of 92.08% and 95.83%, and f1-scores of 0.9207 and 0.9369, respectively. Compared with existing single user identification models, our approach demonstrated accuracy improvements of 9.3% and 36.76% for each database. These findings underscore the potential of the new 2-stage model for enhancing the practicality of ECG-based user identification systems and provide a promising foundation for future research on deep learning signal processing.