论文标题
通过深神经网络的心电图分类数据增强
Data Augmentation for Electrocardiogram Classification with Deep Neural Network
论文作者
论文摘要
心电图（ECG）是诊断心血管事件的最关键监测方式。精确和自动检测异常的心电图模式对医生和患者都是有益的。在自动检测异常心电图模式时，深度神经网络（DNN）已显示出显着的成就。但是，DNN需要大量标记的数据，这些数据通常很昂贵。另一方面，最近的研究通过随机组合数据可以提高图像分类精度。因此，在这项工作中，我们探讨了适用于心电图数据的数据增强，并提出了ECG增强。我们通过引入ECG增强来显示，我们可以通过单铅ECG数据改善房颤的分类，而无需更改DNN的体系结构。
Electrocardiogram (ECG) is the most crucial monitoring modality to diagnose cardiovascular events. Precise and automatic detection of abnormal ECG patterns is beneficial to both physicians and patients. In the automatic detection of abnormal ECG patterns, deep neural networks (DNNs) have shown significant achievements. However, DNNs require large amount of labeled data, which are often expensive to obtain. On the other hand, recent research have shown by randomly combining data augmentations can improve image classification accuracy. Thus, in this work we explore data augmentation suitable for ECG data and propose ECG Augment. We show by introducing ECG Augment, we can improve classification of atrial fibrillation with single lead ECG data, without changing an architecture of DNN.
