用图像化方法分析心电图，准确率超99%
GAF-FusionNet: Multimodal ECG Analysis via Gramian Angular Fields and Split Attention
- 将心电图转为图像，结合注意力机制融合多模态特征
- 在三个数据集上准确率达94.5%至99.6%
- 适合心血管疾病智能诊断研究者参考
心电图分析在心血管疾病诊断中至关重要，但复杂信号的精准解读仍具挑战。本文提出一种新型多模态框架GAF-FusionNet，通过格拉米安角场（Gramian Angular Fields, GAF）将时间序列转化为图像表示，并引入双层跨通道分割注意力模块，自适应融合时序与空间特征，实现互补信息的精细整合。我们在三个不同的心电图数据集上评估该模型：ECG200、ECG5000 和 MIT-BIH Arrhythmia Database。结果表明，该模型在各项任务中均显著优于现有方法，在三个数据集上的分类准确率分别达到94.5%、96.9%和99.6%。代码即将开源。
原文摘要 · Abstract (English)
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.
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