Chen Ming, Wang GuiJin, Xie PengWei, Sang ZhenHua, Lv TingTing, Zhang Ping, Yang HuaZhong
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2559-2562. doi: 10.1109/EMBC.2018.8512789.
Detection of ECG characteristic points serves as the first step in automated ECG analysis techniques. We propose a novel end-to-end deep learning scheme called Region Aggregation Network (RAN) for ECG characteristic points de- tection. A 1D Convolutional Neural Network (CNN) is adopted to automatically process ECG signals. A novel strategy of Region Aggregation is proposed to replace the conventional fully connected layer as regressor. Our work provides robust and accurate detection performance on public ECG database. The evaluation results of our method on QT database show comparable detection accuracy compared with state-of-the-art works.
心电图特征点的检测是自动心电图分析技术的第一步。我们提出了一种名为区域聚合网络（RAN）的新型端到端深度学习方案用于心电图特征点检测。采用一维卷积神经网络（CNN）自动处理心电图信号。提出了一种新颖的区域聚合策略来取代传统的全连接层作为回归器。我们的工作在公共心电图数据库上提供了强大而准确的检测性能。我们的方法在QT数据库上的评估结果显示，与现有最先进的方法相比，检测准确率相当。
