提出可解释的统一心电特征，提升跨医院模型泛化能力。
Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction
- 从原始波形直接提取形态与时频特征，构建统一特征空间。
- 跨数据集测试下AUROC保持在0.74-0.78，优于传统方法。
- 适合需要可解释性与真实外部验证的临床预测场景。
心电图广泛用于心血管风险预测，但因设备协议、人群和测量差异，模型跨医院迁移常失败。本研究在MIMIC-IV与阿尔伯塔队列上，针对心衰分类、30天全因死亡率及窦性心律患者30天死亡率三个任务，评估跨数据集泛化能力。为减少厂商测量偏差，构建基于原始波形的和谐化、可解释特征表示：包含特征数据库的心电形态/心率变异性摘要，以及紧凑的时频描述符（自回归与小波特征）。在该统一特征空间上训练XGBoost模型，并采用患者互斥的内部与双向外部测试。预设两个假设：（H1）外部测试的AUROC不低于源站点内部的90%；（H2）和谐特征集的内部AUROC与本地机器测量模型相差不超过10%。结果表明，内部AUROC为0.79-0.82，跨数据集AUROC为0.74-0.78，且转移后AUPRC变化更大且具方向性。作为探索性基准，直接在原始波形上训练的端到端ConvNeXt模型（含年龄与性别）内部性能更高，但和谐特征在跨数据集稳定性上仍具竞争力。研究证明，一致的波形衍生特征接口能维持性能，支持现实外部验证，并为跨院临床预测提供透明替代方案。
原文摘要 · Abstract (English)
Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail to transfer across hospitals because of protocol, population, and measurement differences. We benchmark cross-dataset generalization on three tasks - heart failure classification, 30-day all-cause mortality, and 30-day mortality among sinus-rhythm ECGs - using two large cohorts (MIMIC-IV and the Alberta Cohort). To reduce vendor-specific measurement mismatch, we build a harmonized, interpretable feature representation computed directly from raw waveforms: FeatureDB morphology/heart-rate-variability summaries plus compact time-frequency descriptors (autoregressive and wavelet features). We train XGBoost models on this unified feature space and evaluate with patient-disjoint internal and bidirectional external testing. We pre-specify two hypotheses: (H1) external AUROC retains at least 90% of source-site internal AUROC under transfer, and (H2) internal AUROC of the harmonized feature set stays within 10% of dataset-native machine-measurement models. Across tasks, internal AUROC is 0.79-0.82 and cross-dataset AUROC is 0.74-0.78, with larger and direction-dependent AUPRC shifts under transfer. As an exploratory benchmark, an end-to-end ConvNeXt model trained directly on raw ECG waveforms with age and sex achieves higher internal AUROC, while the harmonized representation remains competitive in relative cross-dataset transfer stability. These findings show that a consistent waveform-derived feature interface preserves performance, supports realistic external validation, and provides a transparent alternative for cross-site clinical prediction.
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