发表机构
University of Connecticut（康涅狄格大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对房颤发作提前一小时预测问题，提出融合TCN与Mamba的AF-Mamba混合架构，实现高效长期RR间期建模，在多项指标上取得优异性能并兼顾效率。
AI 中文摘要
心房颤动（AF）是最常见的心律失常，与卒中和心力衰竭风险增加相关。可穿戴和便携式心电图监测设备的日益普及，使得在临床环境之外能够持续评估心脏节律。在房颤发作前进行预测，可为及时的临床评估提供额外的提前时间，并可能改善有房颤相关并发症风险患者的管理。本研究聚焦于利用长期RR间期（RRIs）提前一小时预测房颤发作。为应对这一挑战，我们提出了一种深度学习架构，该架构将用于局部特征编码的时间卷积网络（TCNs）与Mamba（一种能够进行长序列建模的选择性状态空间模型）相结合。这种混合TCN-Mamba设计能够在小时级输入窗口上进行高效训练和推理，克服了Transformer二次方扩展和循环网络梯度消失的局限性。在受试者级别的5折测试中，所提出的模型达到了0.889的敏感性、0.943的特异性、0.813的F1分数、0.974的AUROC和0.933的AUPRC。在配对的跨数据集保留评估中，AF-Mamba在未见过的AF和NSR数据集上保持了判别性能，平均AUROC为0.897。与最先进的房颤预测模型和通用时间序列模型相比，AF-Mamba在实现有竞争力的预测性能的同时，为长RRI序列提供了良好的性能-效率权衡。这些发现证明了AF-Mamba在提前一小时准确预测房颤以及实时连续动态监测方面的潜力。
英文摘要
Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with increased risks of stroke and heart failure. The growing availability of wearable and portable ECG monitoring enables continuous assessment of cardiac rhythm outside clinical settings. Predicting AF before its onset could provide additional lead time for timely clinical assessment and potentially improve the management of patients at risk of AF-related complications. This study focuses on predicting AF onset one hour in advance using long-term RR intervals (RRIs). To address this challenge, we propose a deep learning architecture that integrates temporal convolutional networks (TCNs) for local features encoding with Mamba, a selective state-space model capable of long-range sequence modeling. This hybrid TCN-Mamba design enables efficient training and inference on one-hour input windows, overcoming limitations of Transformers' quadratic scaling and recurrent networks' vanishing gradients. In subject-wise 5-fold testing, the proposed model achieved a sensitivity of 0.889, specificity of 0.943, F1-score of 0.813, AUROC of 0.974, and AUPRC of 0.933. In paired cross-dataset holdout evaluation, AF-Mamba maintained discriminative performance across unseen AF and NSR datasets, achieving a mean AUROC of 0.897. Compared against state-of-the-art AF prediction models and general time-series models, AF-Mamba achieved competitive predictive performance while providing a favorable performance-efficiency trade-off for long RRI sequences. These findings demonstrate the potential of AF-Mamba for accurate AF prediction one hour in advance and real-time continuous ambulatory monitoring.
Comments11 pages, 5 figures, 7 tables. Extended version of IEEE BSN 2025. Code: https://github.com/yongbin98/AF_Mamba
