Recent advances of Internet of Medical Things have allowed for continuous heart rhythm monitoring in a comfortable fashion. Single lead Electrocardiograph (ECG) is first collected by the wearable devices, and then some intelligent approaches are employed for automatic recognition of heart rhythms. Because the single lead ECG wave is different from traditional 12-leads Holter-based ECG signal in terms of high noise/artifact and the missing of other channels, specific algorithms for pattern recognition of the single lead ECG waves have been proposed in recent years. This paper systematically surveys state-of-the-art methods for screening atrial fibrillation from a single lead ECG wave. The database and performance metrics for this problem are demonstrated, the data preprocessing and feature extraction techniques are collected, and then the learning methods in terms of machine learning and deep learning are comparatively summarized. Specifically, the techniques for data preprocessing are reviewed and the most common and powerful features are listed, which are capable of providing a guideline for researchers aiming at developing AF detection algorithms. Finally, we discuss the potential contributors that are probably helpful for screening the atrial fibrillation from a single lead ECG wave.

医疗物联网的最新进展允许以舒适的方式连续监测心律。单导联心电图（ECG）首先由可穿戴设备采集，然后采用一些智能方法自动识别心律。由于单导联心电波不同于传统的基于12导联Holter的心电信号，存在高噪声/伪影和其他通道缺失等问题，近年来提出了具体的单导联心电波模式识别算法。本文系统地调查了从单导联 ECG 波筛查房颤的最先进方法。展示了该问题的数据库和性能指标，收集了数据预处理和特征提取技术，然后比较总结机器学习和深度学习方面的学习方法。具体而言，回顾了数据预处理技术，并列出了最常见和最强大的功能，可为旨在开发 AF 检测算法的研究人员提供指导。最后，我们讨论了可能有助于从单导联 ECG 波筛查房颤的潜在因素。