Abstract: Cardiovascular diseases seriously threaten human health, and applying deep learning to ECG analysis can significantly improve diagnostic accuracy. However, existing ECG classification algorithms often lack effective modeling for multi-resolution temporal features and channel coordination. This paper proposes TCC-ResNeXt, a multi-scale temporal and channel-coordinated ECG classification algorithm. The method combines a Period-Adaptive Module (PAM) for extracting complex temporal features and an ECG-ACmix module for adaptively fusing multi-head attention with convolutional features across channels. Experiments on CPSC-2018, Chapman, and DS-COM datasets show that the proposed approach achieves superior performance, with average F1 scores of 0.798, 0.968, and 0.751, respectively, outperforming methods like MobileNetV3, MVMSNet, and EcgTransformer in AUC, Recall, and F1. These results confirm the effectiveness of TCC-ResNeXt for automated ECG classification and intelligent cardiovascular disease diagnosis.In addition, the framework demonstrates strong generalization and robustness across datasets. It provides a promising direction for practical clinical ECG analysis and real-world deployment.

摘要： 心血管疾病严重威胁人类生命健康，将深度学习方法应用于医学心电信号领域能够提高诊疗水平。现有心电分类算法虽在特征提取方面取得一定进展，但对多分辨率时序特征与跨通道协同关系的关联建模仍存在不足。提出一种基于Temporal Channel Coordinated-ResNeXt（TCC-ResNeXt）的多尺度时序特征与通道协同心电分类算法。首先，通过设计周期自适应的多分辨率时序调制模块PAM，有效提取心电信号中复杂的时序特征；同时，引入为心电信号设计的ECG-ACmix模块，在轻量化参数的基础上，通过多头通道注意力与卷积特征的自适应加权融合，实现在多导联心电数据中对各通道特征的增强，有力刻画通道间的依赖关系。实验结果表明，所提算法在CPSC-2018、Chapman和DS-COM三个数据集上均取得了优异的表现，平均F1分数分别达到0.798、0.968和0.751，和其他方法(如MobileNetV3、MVMSNet、EcgTransformer)相比，TCC-ResNeXt在AUC、Recall和F1分数上均优于其他算法。实验验证了该算法在心电信号分类任务上的优越性能，为心血管疾病的智能诊断提供了新的解决方案。