Department of Electrical and Electronics Engineering, Firat University, Elazig 23000, Turkey.
Department of Mechanical Engineering, Bartin University, Bartin 74100, Turkey.
Int J Environ Res Public Health. 2021 Oct 28;18(21):11302. doi: 10.3390/ijerph182111302.
Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diagnosis systems are developed using artificial intelligence techniques for automated detection of AF. Various machine learning and deep learning (DL) techniques have been developed for the automated detection of AF. In this review, we focused on the automated AF detection models developed using DL techniques. Twenty-four relevant articles published in international journals were reviewed. DL models based on deep neural network, convolutional neural network (CNN), recurrent neural network, long short-term memory, and hybrid structures were discussed. Our analysis showed that the majority of the studies used CNN models, which yielded the highest detection performance using ECG and heart rate variability signals. Details of the ECG databases used in the studies, performance metrics of the various models deployed, associated advantages and limitations, as well as proposed future work were summarized and discussed. This review paper serves as a useful resource for the researchers interested in developing innovative computer-assisted ECG-based DL approaches for AF detection.
心房颤动（AF）是一种常见的心律失常，可导致中风、心力衰竭和过早死亡。心电图（ECG）上的 AF 手动筛查既耗时又容易出错。为了克服这些限制，使用人工智能技术开发了计算机辅助诊断系统，用于自动检测 AF。已经开发了各种机器学习和深度学习（DL）技术，用于自动检测 AF。在本次综述中，我们重点关注使用 DL 技术开发的自动 AF 检测模型。综述了在国际期刊上发表的 24 篇相关文章。讨论了基于深度神经网络、卷积神经网络（CNN）、递归神经网络、长短时记忆和混合结构的 DL 模型。我们的分析表明，大多数研究都使用了 CNN 模型，这些模型使用 ECG 和心率变异性信号可获得最高的检测性能。总结并讨论了研究中使用的 ECG 数据库的详细信息、各种部署模型的性能指标、相关的优点和局限性，以及提出的未来工作。本文综述为有兴趣开发基于 ECG 的创新计算机辅助 DL 方法来检测 AF 的研究人员提供了有价值的资源。
