Xie Chenggong, Wang Zhao, Yang Chenglong, Liu Jianhe, Liang Hao
Hunan Provincial Key Laboratory of TCM Diagnostics, Hunan University of Chinese Medicine, 410208 Changsha, Hunan, China.
School of Acupuncture and Tui-na and Rehabilitation, Hunan University of Chinese Medicine, 410208 Changsha, Hunan, China.
Rev Cardiovasc Med. 2024 Jan 8;25(1):8. doi: 10.31083/j.rcm2501008. eCollection 2024 Jan.
Atrial fibrillation (AF) is a common arrhythmia that can result in adverse cardiovascular outcomes but is often difficult to detect. The use of machine learning (ML) algorithms for detecting AF has become increasingly prevalent in recent years. This study aims to systematically evaluate and summarize the overall diagnostic accuracy of the ML algorithms in detecting AF in electrocardiogram (ECG) signals.
The searched databases included PubMed, Web of Science, Embase, and Google Scholar. The selected studies were subjected to a meta-analysis of diagnostic accuracy to synthesize the sensitivity and specificity.
A total of 14 studies were included, and the forest plot of the meta-analysis showed that the pooled sensitivity and specificity were 97% (95% confidence interval [CI]: 0.94-0.99) and 97% (95% CI: 0.95-0.99), respectively. Compared to traditional machine learning (TML) algorithms (sensitivity: 91.5%), deep learning (DL) algorithms (sensitivity: 98.1%) showed superior performance. Using multiple datasets and public datasets alone or in combination demonstrated slightly better performance than using a single dataset and proprietary datasets.
ML algorithms are effective for detecting AF from ECGs. DL algorithms, particularly those based on convolutional neural networks (CNN), demonstrate superior performance in AF detection compared to TML algorithms. The integration of ML algorithms can help wearable devices diagnose AF earlier.
心房颤动（AF）是一种常见的心律失常，可导致不良心血管结局，但往往难以检测。近年来，使用机器学习（ML）算法检测AF变得越来越普遍。本研究旨在系统评价和总结ML算法在心电图（ECG）信号中检测AF的总体诊断准确性。
检索的数据库包括PubMed、Web of Science、Embase和谷歌学术。对所选研究进行诊断准确性的荟萃分析，以综合敏感性和特异性。
共纳入14项研究，荟萃分析的森林图显示，合并敏感性和特异性分别为97%（95%置信区间[CI]：0.94-0.99）和97%（95%CI：0.95-0.99）。与传统机器学习（TML）算法（敏感性：91.5%）相比，深度学习（DL）算法（敏感性：98.1%）表现更优。单独或联合使用多个数据集和公共数据集的性能略优于使用单个数据集和专有数据集。
ML算法对从ECG中检测AF有效。与TML算法相比，DL算法，尤其是基于卷积神经网络（CNN）的算法，在AF检测中表现更优。ML算法的整合有助于可穿戴设备更早地诊断AF。
