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

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 e xplicitly tailored for cross - state (rest - exercise) conditions. The proposed model creatively combines multi - scale d eep c onvolu-tional feature extraction with attention mechanisms to ensure strong identification across different physiological states. Experim ental 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 - exercis e ECG) and 94.72% in the Exercise - t o - Rest scenario (training on post - exercis e ECG and testing on rest ing 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 it s potential as a practical solution for post - exercise ECG - based authentication in dynamic real - world settings.