基于ArcFace-Inception的ECG生物识别：在MIMIC和HEEDB上的外部验证
ECG Biometrics with ArcFace-Inception: External Validation on MIMIC and HEEDB
AI总结：
本文评估了在MIMIC-IV-ECG和HEEDB上基于ArcFace-Inception模型的ECG生物识别性能，发现其在大规模封闭集条件下仍具有效性，但受领域异质性、时间漂移、画廊大小等因素影响显著。
摘要：
ECG生物识别主要在小样本和短会话间隔上研究，未解决在大规模画廊、外部领域偏移和多年时间间隔下的识别行为。我们评估了一个在内部临床语料库上训练的1D Inception-v1模型，该模型使用ArcFace进行训练，包含164,440个12导联ECG数据，来自53,079名患者。该模型在MIMIC-IV-ECG和HEEDB上进行了测试。研究使用了统一的封闭集留一法协议，结合Rank@K和TAR@FAR指标，以及缩放、时间压力、重新排序和置信度分析。在一般可比性下，系统在ASUGI-DB上达到Rank@1为0.9506，在MIMIC-GC上为0.8291，在HEEDB-GC上为0.6884。在时间压力测试中，当画廊大小恒定时，Rank@1从MIMIC的0.7853下降到0.6433，从HEEDB的0.6864下降到0.5560（1到5年）。HEEDB上的缩放分析显示，随着画廊大小增加，性能单调下降，但当每个患者有更多的检查时，性能恢复。在HEEDB-RR上，事后重新排序进一步提高了检索效果，AS-norm达到Rank@1=0.8005，从基线0.7765提升。因此，ECG身份信息在外部验证的大规模封闭集条件下仍可测量，但其操作质量受到领域异质性、纵向漂移、画廊大小和第二阶段分数处理的强烈影响。
英文摘要：
ECG biometrics has been studied mainly on small cohorts and short inter-session intervals, leaving open how identification behaves under large galleries, external domain shift, and multi-year temporal gaps. We evaluated a 1D Inception-v1 model trained with ArcFace on an internal clinical corpus of 164,440 12-lead ECGs from 53,079 patients and tested it on larger cohorts derived from MIMIC-IV-ECG and HEEDB. The study used a unified closed-set leave-one-out protocol with Rank@K and TAR@FAR metrics, together with scale, temporal-stress, reranking, and confidence analyses. Under general comparability, the system achieved Rank@1 of 0.9506 on ASUGI-DB, 0.8291 on MIMIC-GC, and 0.6884 on HEEDB-GC. In the temporal stress test at constant gallery size, Rank@1 declined from 0.7853 to 0.6433 on MIMIC and from 0.6864 to 0.5560 on HEEDB from 1 to 5 years. Scale analysis on HEEDB showed monotonic degradation as gallery size increased and recovery as more examinations per patient became available. On HEEDB-RR, post-hoc reranking further improved retrieval, with AS-norm reaching Rank@1 = 0.8005 from a 0.7765 baseline. ECG identity information therefore remains measurable under externally validated large-scale closed-set conditions, but its operational quality is strongly affected by domain heterogeneity, longitudinal drift, gallery size, and second-stage score processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
