# CrossStateECG: Multi-Scale Deep Convolutional Network with Attention for Rest-Exercise ECG Biometrics

arXiv: https://arxiv.org/abs/2510.17467 · HTML: https://mlsanity.com/paper/2510.17467/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [ECG Identification under Exercise and Rest Situations via Various Learning Methods](https://mlsanity.com/paper/1905.04442/) (2019)
- [ECG-Based Patient Identification: A Comprehensive Evaluation Across Health and Activity Conditions](https://mlsanity.com/paper/2302.06529/) (2023)
- [DE-PADA: Personalized Augmentation and Domain Adaptation for ECG Biometrics Across Physiological States](https://mlsanity.com/paper/2502.04973/) (2025)

## Abstract (arXiv)

Current research in Electrocardiogram (ECG) biometrics mainly emphasizes resting-state conditions, leaving the performance decline in rest-exercise scenarios largely unresolved. This paper introduces CrossStateECG, a robust ECG-based authentication model explicitly tailored for cross-state (rest-exercise) conditions. The proposed model creatively combines multi-scale deep convolutional feature extraction with attention mechanisms to ensure strong identification across different physiological states. Experimental results on the exercise-ECGID dataset validate the effectiveness of CrossStateECG, achieving an identification accuracy of 92.50% in the Rest-to-Exercise scenario (training on resting ECG and testing on post-exercise ECG) and 94.72% in the Exercise-to-Rest scenario (training on post-exercise ECG and testing on resting ECG). Furthermore, CrossStateECG demonstrates exceptional performance across both state combinations, reaching an accuracy of 99.94% in Rest-to-Rest scenarios and 97.85% in Mixed-to-Mixed scenarios. Additional validations on the ECG-ID and MIT-BIH datasets further confirmed the generalization abilities of CrossStateECG, underscoring its potential as a practical solution for post-exercise ECG-based authentication in dynamic real-world settings.