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

TL;DR

This paper presents CrossStateECG, a deep learning model that effectively authenticates individuals using ECG data across rest and exercise states, outperforming existing methods and demonstrating strong generalization on multiple datasets.

Contribution

It introduces a novel multi-scale deep convolutional network with attention mechanisms specifically designed for cross-state ECG biometrics, addressing a key challenge in dynamic authentication scenarios.

Findings

Achieves over 92% accuracy in cross-state identification scenarios.

Demonstrates near-perfect accuracy in same-state scenarios with 99.94%.

Validates generalization on multiple datasets, including ECG-ID and MIT-BIH.

Abstract

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.