用注意力机制让心电图模型能看懂心脏的时序模式，还看得懂理由。
CardioPatternFormer: Pattern-Guided Attention for Interpretable ECG Classification with Transformer Architecture
- 用模式引导的注意力机制识别心电图中的复杂波形模式
- 在多病共存的复杂心电图上分类准确率高，能发现细微异常
- 通过注意力热力图解释判断依据，适合临床医生信任使用
精准的心电图解读至关重要，但复杂的心脏数据与“黑箱”AI模型限制了其临床应用。受Transformer在自然语言处理中解析序列数据的成功启发，我们将心电图视为心脏独特的“时序语言”。本文提出CardioPatternFormer，一种基于Transformer的可解释心电图分类新模型。该模型采用先进的注意力机制，精确识别并分类多种心脏节律模式，擅长捕捉细微异常并区分多重共存疾病。其模式引导的注意力机制能清晰揭示影响判断的关键信号区域，实现“心脏发声”的透明化解释。CardioPatternFormer在复杂心电图（包括多病共存情况）上表现稳健，注意力图使临床医生可理解模型决策逻辑，增强信任并辅助诊断。本工作为心电图分析提供了强大且透明的解决方案，推动更可靠、可落地的医疗AI在心脏病学中的应用。
原文摘要 · Abstract (English)
Accurate ECG interpretation is vital, yet complex cardiac data and "black-box" AI models limit clinical utility. Inspired by Transformer architectures' success in NLP for understanding sequential data, we frame ECG as the heart's unique "language" of temporal patterns. We present CardioPatternFormer, a novel Transformer-based model for interpretable ECG classification. It employs a sophisticated attention mechanism to precisely identify and classify diverse cardiac patterns, excelling at discerning subtle anomalies and distinguishing multiple co-occurring conditions. This pattern-guided attention provides clear insights by highlighting influential signal regions, effectively allowing the "heart to talk" through transparent interpretations. CardioPatternFormer demonstrates robust performance on challenging ECGs, including complex multi-pathology cases. Its interpretability via attention maps enables clinicians to understand the model's rationale, fostering trust and aiding informed diagnostic decisions. This work offers a powerful, transparent solution for advanced ECG analysis, paving the way for more reliable and clinically actionable AI in cardiology.
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