论文标题
通过最佳运输增强，心电图数据不平衡的心脏病诊断
Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation
论文作者
论文摘要
在本文中，我们关注一种新的数据增强方法，以解决不平衡的ECG数据集中的数据不平衡问题，以提高心脏病检测的鲁棒性和准确性。通过使用最佳运输，我们可以从正常的心电图中增加心电图疾病数据，以平衡不同类别之间的数据。我们构建了一个多功能变压器（MF转换器）作为我们的分类模型，其中从时间和频域中提取不同的功能以诊断各种心脏状况。从12铅ECG信号中学习，我们的模型能够区分五种心脏条件。我们的结果表明1）分类模型在五个ECG类别上做出竞争性预测的能力； 2）改善准确性和鲁棒性，反映了我们数据增强方法的有效性。
In this paper, we focus on a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets to improve the robustness and accuracy of heart disease detection. By using Optimal Transport, we augment the ECG disease data from normal ECG beats to balance the data among different categories. We build a Multi-Feature Transformer (MF-Transformer) as our classification model, where different features are extracted from both time and frequency domains to diagnose various heart conditions. Learning from 12-lead ECG signals, our model is able to distinguish five categories of cardiac conditions. Our results demonstrate 1) the classification models' ability to make competitive predictions on five ECG categories; 2) improvements in accuracy and robustness reflecting the effectiveness of our data augmentation method.
