跨状态心电生物识别模型，提升运动后身份验证准确率
CrossStateECG: Multi-Scale Deep Convolutional Network with Attention for Rest-Exercise ECG Biometrics
- 融合多尺度卷积与注意力机制，捕捉不同生理状态下的心电特征
- 在静息-运动和运动-静息场景下分别达到92.50%和94.72%准确率
- 适用于真实动态环境，对运动前后心电信号变化鲁棒
当前心电生物识别研究主要集中于静息状态，难以应对静息-运动状态转换下的性能下降问题。本文提出CrossStateECG，一种专为跨状态（静息-运动）设计的鲁棒心电身份认证模型。该模型创新性地结合多尺度深度卷积特征提取与注意力机制，确保在不同生理状态下保持强识别能力。在exercise-ECGID数据集上的实验验证了其有效性：在静息训练、运动测试场景下识别准确率达92.50%，运动训练、静息测试场景下达94.72%；在静息-静息场景中准确率为99.94%，混合状态间识别达97.85%。在ECG-ID和MIT-BIH数据集上的额外验证进一步证实了CrossStateECG的泛化能力，表明其具备在动态真实场景中实现运动后心电身份认证的实用潜力。
原文摘要 · Abstract (English)
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.
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