用时间局部聚类解析CNN的心电图预测逻辑
Explaining deep learning for ECG using time-localized clusters
- 从模型内部表征提取时间局部聚类，划分心电图特征区域
- 量化表示不确定性，揭示各波段对预测的贡献度
- 适合临床医生理解AI诊断依据，提升可信度
深度学习显著推进了心电图（ECG）分析，实现自动标注、疾病筛查和预后判断，超越传统临床能力。然而，模型的理解仍具挑战性，限制了可解释性与知识发现。本文提出一种针对卷积神经网络在心电图分析中的新可解释性方法，通过从模型内部表征中提取时间局部聚类，按学习到的特征对心电图进行分割，并量化这些表征的不确定性。该方法可可视化不同波形区域如何影响模型预测，评估决策的确定性。通过提供结构化且可解释的深度学习视图，本方法增强了人工智能辅助诊断的信任度，并有助于发现具有临床意义的电生理模式。
原文摘要 · Abstract (English)
Deep learning has significantly advanced electrocardiogram (ECG) analysis, enabling automatic annotation, disease screening, and prognosis beyond traditional clinical capabilities. However, understanding these models remains a challenge, limiting interpretation and gaining knowledge from these developments. In this work, we propose a novel interpretability method for convolutional neural networks applied to ECG analysis. Our approach extracts time-localized clusters from the model's internal representations, segmenting the ECG according to the learned characteristics while quantifying the uncertainty of these representations. This allows us to visualize how different waveform regions contribute to the model's predictions and assess the certainty of its decisions. By providing a structured and interpretable view of deep learning models for ECG, our method enhances trust in AI-driven diagnostics and facilitates the discovery of clinically relevant electrophysiological patterns.
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