Cardiovascular disease (CVD) is a general term for a series of heart or blood vessels abnormality that serves as a global leading reason for death. The earlier the abnormal heart rhythm is discovered, the less severe the sequela and the faster the recovery. Electrocardiogram (ECG), as a main way to detect the electrical activity of heart, is a very important harmless means of predicting and diagnosing CVDs. However, ECG signal has characteristics of complex and high chaos, making it time-consuming and exhausting to interpret ECG signal even for experts. Hence, computer-aided methods are required to relief human burden and reduce errors caused by tiredness, inter- and intra-difference. Deep learning shows outstanding performance on ECG classification studies recent few years. Its hierarchical architecture enables higher-level features obtained and its strong ability to feature extraction contributes to classification project. Latest studies can achieve higher accuracy and efficiency than manual classification by experts. In this paper, we review the existing studies of deep learning applied in ECG diagnosis according to four typical algorithms: stacked auto-encoders, deep belief network, convolutional neural network and recurrent neural network. We first introduced the mechanism, development and application of the algorithms. Then we review their applications in ECG diagnosis systematically, discussing their highlights and limitations. Our view about future potential development of deep learning in ECG diagnosis is stated in the final part of this paper.

心血管疾病（CVD）是一系列心脏或血管异常的总称，是全球主要的死亡原因。越早发现异常心律，后遗症越轻，恢复越快。心电图（ECG）作为检测心脏电活动的主要方式，是预测和诊断心血管疾病的一种非常重要的无害手段。然而，心电信号具有复杂、高度混沌的特点，即使对于专家来说，解读心电信号也费时费力。因此，需要计算机辅助方法来减轻人类的负担并减少因疲劳、内部差异和内部差异引起的错误。近年来，深度学习在 ECG 分类研究中表现出色。其分层架构能够获得更高级别的特征，其强大的特征提取能力有助于分类项目。最新研究可以达到比专家手动分类更高的准确性和效率。在本文中，我们根据四种典型算法：堆叠自动编码器、深度信念网络、卷积神经网络和循环神经网络，回顾了现有的深度学习应用于心电图诊断的研究。我们首先介绍了算法的机制、发展和应用。然后我们系统地回顾了它们在心电图诊断中的应用，讨论了它们的亮点和局限性。我们对深度学习在心电图诊断中的未来潜在发展的看法在本文的最后部分进行了陈述。