AI 中文总结
本研究提出NeuroECG，利用预训练心电图基础模型ECGFounder进行微调，结合分位数池化和PCA，在心脏骤停后无需脑电图的情况下，通过床旁心电图深度特征与临床协变量融合，实现高精度神经预后预测（AUROC 0.8077）。
AI 中文摘要
心脏骤停后的神经预后评估通常依赖于脑电图（EEG）。然而，脑电图需要较高的临床资源。床旁心电图（ECG）是标准且低成本的检查。但其在预测神经预后方面的价值仍未得到充分探索。在本研究中，我们提出了NeuroECG，一种基于ECGFounder的深度表示框架，用于无需脑电图的辅助预后评估。NeuroECG通过任务特定的微调来适应预训练的心电图基础模型。我们在单通道床旁监测心电图上实施了逐步解冻策略。每个患者的多个心电图片段被编码为片段级深度特征。这些嵌入通过分位数池化（q = 0.24）进行聚合，并使用主成分分析（PCA）进行压缩。在来自多中心I-CARE数据库的412名有心电图数据的患者上的实验表明，适应后的ECGFounder骨干在仅使用心电图的骨干基线中取得了最佳性能，测试AUROC为0.7333。我们进一步将学习到的深度心电图表示与静态临床协变量相结合。所提出的NeuroECG模型实现了测试AUROC为0.8077和AUPRC为0.8970。这些结果支持深度床旁心电图表示作为辅助预后信息的有用来源。它们与静态临床协变量的整合在无需脑电图的环境中改善了预测。源代码可在以下网址获取：此https URL
英文摘要
Neurological prognostication after cardiac arrest commonly relies on electroencephalography (EEG). However, EEG demands high clinical resources. Bedside electrocardiography (ECG) is standard and low-cost. Yet, its value for predicting neurological outcomes remains underexplored. In this study, we propose NeuroECG, an ECGFounder-based deep representation framework for EEG-free auxiliary prognostication. NeuroECG adapts a pretrained ECG foundation model via task-specific fine-tuning. We implement a gradual unfreezing strategy on single-channel bedside monitoring ECG. Multiple ECG segments per patient are encoded into segment-level deep features. These embeddings are aggregated via quantile pooling (q = 0.24) and compressed using principal component analysis (PCA). Experiments on 412 ECG-available patients from the multicenter I-CARE database show that the adapted ECGFounder backbone achieves the best performance among ECG-only backbone baselines, with a test AUROC of 0.7333. We further combine the learned deep ECG representation with static clinical covariates. The proposed NeuroECG model achieves a test AUROC of 0.8077 and an AUPRC of 0.8970. These results support deep bedside ECG representations as a useful source of auxiliary prognostic information. Their integration with static clinical covariates improves prediction in an EEG-free setting. The source code is available at https://github.com/goddream66/NeuroECG
CommentsAccepted by BIBM 2026
