标题： GAF-FusionNet：通过Gramian角场和分割注意力进行多模态ECG分析 显示英文标题

标题： GAF-FusionNet: Multimodal ECG Analysis via Gramian Angular Fields and Split Attention

摘要： 心电图（ECG）分析在诊断心血管疾病中起着至关重要的作用，但对这些复杂信号的准确解释仍然具有挑战性。 本文介绍了一种新的多模态框架（GAF-FusionNet）用于ECG分类，该框架结合了时间序列分析和基于Gramian Angular Fields（GAF）的图像表示。 我们的方法采用双层跨通道分割注意力模块，以自适应融合时间特征和空间特征，实现互补信息的细致整合。 我们在三个不同的ECG数据集上评估了GAF-FusionNet：ECG200、ECG5000和MIT-BIH心律失常数据库。 结果表明，与最先进的方法相比有显著提升，我们的模型在各自数据集上的准确率分别为94.5%、96.9%和99.6%。 我们的代码将很快在https://github.com/Cross-Innovation-Lab/GAF-FusionNet.git提供。 显示英文摘要

摘要： Electrocardiogram (ECG) analysis plays a crucial role in diagnosing cardiovascular diseases, but accurate interpretation of these complex signals remains challenging. This paper introduces a novel multimodal framework(GAF-FusionNet) for ECG classification that integrates time-series analysis with image-based representation using Gramian Angular Fields (GAF). Our approach employs a dual-layer cross-channel split attention module to adaptively fuse temporal and spatial features, enabling nuanced integration of complementary information. We evaluate GAF-FusionNet on three diverse ECG datasets: ECG200, ECG5000, and the MIT-BIH Arrhythmia Database. Results demonstrate significant improvements over state-of-the-art methods, with our model achieving 94.5\%, 96.9\%, and 99.6\% accuracy on the respective datasets. Our code will soon be available at https://github.com/Cross-Innovation-Lab/GAF-FusionNet.git.