Hear the Heartbeat in Phases: Physiologically Grounded Phase-Aware ECG Biometrics

2026-01-01

AI Summary

Existing ECG-based authentication methods treat the cardiac cycle as a homogeneous signal, overlooking the distinct characteristics of different physiological phases. This work proposes a Hierarchical Phase-Aware Fusion framework (HPAF), which explicitly models the unique features of individual cardiac phases for the first time. Through a three-stage architecture—comprising intra-phase representation, phase-group fusion, and global representation integration—HPAF effectively mitigates cross-phase feature entanglement. Additionally, a heartbeat-aware multi-prototype enrollment strategy is introduced to enhance robustness. Evaluated on three public datasets, the proposed method significantly outperforms state-of-the-art approaches, achieving the best reported performance in both closed-set and open-set authentication scenarios.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Electrocardiography (ECG) is adopted for identity authentication in wearable devices due to its individual-specific characteristics and inherent liveness. However, existing methods often treat heartbeats as homogeneous signals, overlooking the phase-specific characteristics within the cardiac cycle. To address this, we propose a Hierarchical Phase-Aware Fusion~(HPAF) framework that explicitly avoids cross-feature entanglement through a three-stage design. In the first stage, Intra-Phase Representation (IPR) independently extracts representations for each cardiac phase, ensuring that phase-specific morphological and variation cues are preserved without interference from other phases. In the second stage, Phase-Grouped Hierarchical Fusion (PGHF) aggregates physiologically related phases in a structured manner, enabling reliable integration of complementary phase information. In the final stage, Global Representation Fusion (GRF) further combines the grouped representations and adaptively balances their contributions to produce a unified and discriminative identity representation. Moreover, considering ECG signals are continuously acquired, multiple heartbeats can be collected for each individual. We propose a Heartbeat-Aware Multi-prototype (HAM) enrollment strategy, which constructs a multi-prototype gallery template set to reduce the impact of heartbeat-specific noise and variability. Extensive experiments on three public datasets demonstrate that HPAF achieves state-of-the-art results in the comparison with other methods under both closed and open-set settings.

Problem

• ECG biometrics
• cardiac phase
• identity authentication
• physiological signal
• heartbeat variability

Innovation

• Phase-Aware ECG
• Hierarchical Fusion
• Intra-Phase Representation
• Multi-Prototype Enrollment
• Cardiac Cycle Modeling