English(EN) AF-Mamba: Efficient Long-Term Signal Modeling for Early Prediction of Atrial Fibrillation Onset

AF-Mamba模型使用TCN和Mamba进行心房颤动早期预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了AF-Mamba，一种新颖的深度学习架构，用于心房颤动（AF）发作的早期预测。该模型集成了时间卷积网络（TCN）和Mamba（一种选择性状态空间模型），以高效处理长序列的RR间期。AF-Mamba在提前一小时预测AF方面表现出强大的预测性能，实现了高灵敏度和特异性，优于现有模型，同时提供了有利的性能-效率权衡。 AI

影响该模型可以改善心房颤动的早期检测，可能带来更好的患者预后和更有效的远程监控。

排序理由该项目是一篇研究论文，详细介绍了一种用于特定医疗预测任务的新深度学习模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yongbin Lee, Ki H. Chon · 2026-09-10 04:00

AF-Mamba：高效长期信号建模用于心房颤动发作的早期预测

arXiv:2609.06984v1 Announce Type: new Abstract: 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 car…

本文如何被排名

报道来源 [1]

AF-Mamba：高效长期信号建模用于心房颤动发作的早期预测

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