论文标题
可解释的深度学习，以自动诊断12铅心电图
Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram
论文作者
论文摘要
心电图（ECG）是一种用于心血管疾病诊断的可靠，无创的方法。随着心电图检查的快速增长和心脏病专家的不足，精确和自动诊断ECG信号已成为热门研究主题。深度学习方法已经证明了预测性医疗任务的有希望的结果。在本文中，我们开发了一个深层神经网络，用于在12铅ECG记录中对心律不齐的多标签分类。公共12铅ECG数据集的实验显示了我们方法的有效性。所提出的模型在接收器操作特征曲线（AUC）下达到了平均面积为0.970，平均F1得分为0.813。深层模型比从提取的专家功能中学到的4种机器学习方法表现出了出色的性能。此外，与同时使用所有12条线索相比，接受单铅ECG训练的深层模型产生的性能低。在12个线索中，表现最好的线索是铅I，AVR和V5。最后，我们采用了Shapley添加性解释（SHAP）方法来解释该模型在患者水平和人群水平上的行为。我们的代码可在https://github.com/onlyzdd/ecg-diarnatosis上免费获得。
Electrocardiogram (ECG) is a widely used reliable, non-invasive approach for cardiovascular disease diagnosis. With the rapid growth of ECG examinations and the insufficiency of cardiologists, accurate and automatic diagnosis of ECG signals has become a hot research topic. Deep learning methods have demonstrated promising results in predictive healthcare tasks. In this paper, we developed a deep neural network for multi-label classification of cardiac arrhythmias in 12-lead ECG recordings. Experiments on a public 12-lead ECG dataset showed the effectiveness of our method. The proposed model achieved an average area under the receiver operating characteristic curve (AUC) of 0.970 and an average F1 score of 0.813. The deep model showed superior performance than 4 machine learning methods learned from extracted expert features. Besides, the deep models trained on single-lead ECGs produce lower performance than using all 12 leads simultaneously. The best-performing leads are lead I, aVR, and V5 among 12 leads. Finally, we employed the SHapley Additive exPlanations (SHAP) method to interpret the model's behavior at both patient level and population level. Our code is freely available at https://github.com/onlyzdd/ecg-diagnosis.
