ECG classification is important to the diagnosis of cardiovascular disease. This paper develops a robust and accurate algorithm for automatic detection of heart arrhythmias from ECG signals recorded with one lead. A novel model based on the convolutional neural network is proposed to extract low-level and high-level features of short term ECG. In addition, Information-Theoretic Metric Learning is utilized as a final classification model to boost the discrimination abilities of the network trained features. The experimental results over the MIT-BIH arrhythmia database show that the model achieves a comparable performance with most of the state-of-the-art methods and Information-Theoretic Metric Learning further improves the performance. Besides the good accuracy achieved, the proposed method balances different criteria.

心电图分类对心血管疾病的诊断很重要。本文开发了一种强大而准确的算法，用于从一根导线记录的ECG信号中自动检测心律不齐。提出了一种基于卷积神经网络的新型模型，用于提取短期心电图的低水平和高水平特征。另外，信息理论度量学习被用作最终分类模型，以提高网络训练特征的辨别能力。MIT-BIH心律失常数据库上的实验结果表明，该模型在大多数最新方法中均具有可比的性能，而信息理论度量学习则进一步提高了性能。除了获得良好的准确性外，所提出的方法还平衡了不同的标准。