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

arXiv: https://arxiv.org/abs/2601.00170 · HTML: https://mlsanity.com/paper/2601.00170/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 41 references that are in the map.
- First posted on arXiv 1 Jan 2026.

## Builds on (most cited first, 2 of 2)

- [ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors](https://mlsanity.com/paper/2409.05627/) (2024)
- [EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication](https://mlsanity.com/paper/2102.08026/) (2021)

## Abstract (arXiv)

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.