论文标题
通过卷积复发神经网络从心电图中的心律失常检测
Cardiac Arrhythmia Detection from ECG with Convolutional Recurrent Neural Networks
论文作者
论文摘要
除了几种特定类型外，心律不齐并不立即威胁生命。但是，如果不适当治疗，它们会引起严重的并发症。特别是，以快速和不规则的心脏跳动为特征的房颤增加了中风的风险。我们提出了三种神经网络体系结构，以检测单个铅心电图信号的异常节奏。这些体系结构结合了卷积层，以从滑动窗口和经常性层中的心律失常检测提取高级特征，以在不同持续时间的信号上汇总这些特征。我们将神经网络应用于心脏病学挑战的数据集，以及通过加入Physionet上的三个数据库而构建的数据集。我们的体系结构在第一个数据集中的准确度为86.23％，类似于挑战的获胜条目，第二个数据集的精度为92.02％。
Except for a few specific types, cardiac arrhythmias are not immediately life-threatening. However, if not treated appropriately, they can cause serious complications. In particular, atrial fibrillation, which is characterized by fast and irregular heart beats, increases the risk of stroke. We propose three neural network architectures to detect abnormal rhythms from single-lead ECG signals. These architectures combine convolutional layers to extract high-level features pertinent for arrhythmia detection from sliding windows and recurrent layers to aggregate these features over signals of varying durations. We applied the neural networks to the dataset used for the challenge of Computing in Cardiology 2017 and a dataset built by joining three databases available on PhysioNet. Our architectures achieved an accuracy of 86.23% on the first dataset, similar to the winning entries of the challenge, and an accuracy of 92.02% on the second dataset.
