用卷积+注意力融合局部形态与全局节律，提升心电图自动诊断准确率。
A CNN-based Local-Global Self-Attention via Averaged Window Embeddings for Hierarchical ECG Analysis
- 用重叠卷积窗平均嵌入生成查询，精细捕捉心电波形局部特征
- 在CODE-15数据集上优于现有模型，分类性能显著提升
- 适合需要高精度心电图分析的医疗AI研究与临床部署
心血管疾病仍是全球死亡主因，高效诊断工具如心电图（ECG）至关重要。深度学习尤其是变换器（transformers）虽能捕捉波形细节与全局节律模式，但难以有效建模关键的局部形态特征。本文提出一种新型局部-全局注意力心电图模型（LGA-ECG），融合卷积归纳偏置与全局自注意力机制。通过重叠卷积窗口的嵌入平均生成查询，实现细粒度形态分析；同时利用整个序列的键和值建模全局上下文。在CODE-15数据集上的实验表明，LGA-ECG超越现有先进模型，消融实验验证了局部-全局注意力策略的有效性。该设计能有效捕捉心电图信号中的层次化时间依赖性与形态模式，展现出在临床部署中进行鲁棒自动化心电图分类的潜力。
原文摘要 · Abstract (English)
Cardiovascular diseases remain the leading cause of global mortality, emphasizing the critical need for efficient diagnostic tools such as electrocardiograms (ECGs). Recent advancements in deep learning, particularly transformers, have revolutionized ECG analysis by capturing detailed waveform features as well as global rhythm patterns. However, traditional transformers struggle to effectively capture local morphological features that are critical for accurate ECG interpretation. We propose a novel Local-Global Attention ECG model (LGA-ECG) to address this limitation, integrating convolutional inductive biases with global self-attention mechanisms. Our approach extracts queries by averaging embeddings obtained from overlapping convolutional windows, enabling fine-grained morphological analysis, while simultaneously modeling global context through attention to keys and values derived from the entire sequence. Experiments conducted on the CODE-15 dataset demonstrate that LGA-ECG outperforms state-of-the-art models and ablation studies validate the effectiveness of the local-global attention strategy. By capturing the hierarchical temporal dependencies and morphological patterns in ECG signals, this new design showcases its potential for clinical deployment with robust automated ECG classification.
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