发表机构
Information Center, The People’s Hospital of Baoan Shenzhen; School of Computer Science and Engineering, South China University of Technology; Department of Cardiology, The People’s Hospital of Baoan Shenzhen; Institute for Advanced Study, Shenzhen University（深圳市宝安区人民医院信息中心; 华南理工大学计算机科学与工程学院; 深圳市宝安区人民医院心内科; 深圳大学高等研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出DCGCNet模型，可实现任意导联的鲁棒心房颤动检测，在跨数据集评估中AUC均超0.98，性能达到新基准。
AI 中文摘要
【背景与目标】在真实临床场景中，由于导联配置多变、跨数据集分布偏移以及普遍存在的生理和技术伪影，从心电图（ECG）信号中可靠检测心房颤动（AF）仍然具有挑战性。因此，我们开发了一种鲁棒且可泛化的深度学习模型，用于准确检测心房颤动。【方法】我们提出了双码本图协同网络（DCGCNet），这是一种新颖的端到端向量量化变分自编码器，可同时执行心房颤动分类和心电图重构。DCGCNet引入了两个关键组件：（1）用于学习抗噪声表示的局部-全局对比模块；（2）动态优化码本原型的自适应码本向量量化器，以更好地匹配输入数据分布，从而防止码本坍塌并增强泛化能力。【结果】DCGCNet在标准的数据集内部12导联评估中实现了最先进的性能，并在七个不同场景中展示了出色的跨数据集泛化能力，所有情况下均始终达到AUC>0.98。此外，它在真实噪声条件下保持了高诊断准确性，包括基线漂移、工频干扰和肌电伪影。【结论】DCGCNet为鲁棒、可泛化且抗噪声的心房颤动检测建立了新的基准，显示出在真实临床环境中部署的巨大潜力。
英文摘要
\textbf{Background and Objective}: Reliable atrial fibrillation (AF) detection from electrocardiogram (ECG) signals remains challenging in real-world clinical settings due to variable lead configurations, cross-dataset domain shifts, and pervasive physiological and technical artifacts. So we develop a robust and generalizable deep learning model for accurate AF detection.\\ \textbf{Methods}: We propose the Dual-Codebook Graph Collaborative Network (DCGCNet), a novel end-to-end vector-quantized variational autoencoder that jointly performs AF classification and ECG reconstruction. DCGCNet introduces two key components: (1) a Local-Global Contrastive Module for learning noise-invariant representations, and (2) an Adaptive Codebook Vector Quantizer that dynamically refines codebook prototypes to better align with input data distributions, thereby preventing codebook collapse and enhancing generalization.\\ \textbf{Results}: DCGCNet achieves state-of-the-art performance in standard intra-dataset 12-lead evaluation and demonstrates exceptional cross-dataset generalization across seven diverse settings, consistently attaining AUC > 0.98 in all cases. Furthermore, it maintains high diagnostic accuracy under realistic noisy conditions, including baseline wander, powerline interference, and EMG artifacts.\\ \textbf{Conclusions}: DCGCNet establishes a new benchmark for robust, generalizable, and noise-resilient AF detection, showing strong potential for deployment in real-world clinical environments.
