IEEE Rev Biomed Eng. 2021;14:219-239. doi: 10.1109/RBME.2020.2976507. Epub 2021 Jan 22.
Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main causes of morbidity and mortality worldwide. The timely diagnosis of AF is an equally important and challenging task because of its asymptomatic and episodic nature. In this paper, state-of-the-art ECG data-based machine learning models and signal processing techniques applied for auto diagnosis of AF are reviewed. Moreover, key biomarkers of AF on ECG and the common methods and equipment used for the collection of ECG data are discussed. Besides that, the modern wearable and implantable ECG sensing technologies used for gathering AF data are presented briefly. In the end, key challenges associated with the development of auto diagnosis solutions of AF are also highlighted. This is the first review paper of its kind that comprehensively presents a discussion on all these aspects related to AF auto-diagnosis in one place. It is observed that there is a dire need for low energy and low cost but accurate auto diagnosis solutions for the proactive management of AF.
心房颤动（AF）是最常见的心律失常类型之一，也是全球发病率和死亡率的主要原因之一。由于其无症状和间歇性的特点，及时诊断 AF 同样是一项重要且具有挑战性的任务。本文回顾了基于心电图数据的最先进的机器学习模型和信号处理技术在自动诊断 AF 中的应用。此外，还讨论了 ECG 上 AF 的关键生物标志物以及用于采集 ECG 数据的常见方法和设备。除此之外，还简要介绍了用于采集 AF 数据的现代可穿戴和可植入式 ECG 传感技术。最后，还强调了与开发 AF 自动诊断解决方案相关的主要挑战。这是第一篇此类综述论文，它全面地讨论了与 AF 自动诊断相关的所有方面。人们发现，非常需要低能耗、低成本但准确的自动诊断解决方案，以便对 AF 进行主动管理。
