Machine Learning for ECG Diagnosis of LV Dysfunction.
Machine Learning for ECG Diagnosis of LV Dysfunction.
复制标题
机器学习用于心电图诊断左心室功能障碍。
DOI：
10.1016/j.jcmg.2021.05.015
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Davies RH
中科院分区：
文献类型：
--
作者：
Davies RH
自1887年奥古斯都·沃勒首次报告人类心电图以来(1)，它在诊断方面的应用不断扩大。心电图提供了一个了解心脏电活动的窗口，可以记录和分类心律失常，随后很快就可以估计心房和心室的大小[2]。几年后，与心肌梗死相关的动态变化被发现，并被证明与临床和组织病理学变化相关。研究人员继续从这种简单的床边测试中提取各种临床有用的数据，展示了其中包含的丰富信息，并使心电在临床医学中无处不在。为了从体表心电中收集更多信息，最近将机器学习方法应用于心电分析。虽然自动心电分析的第一次尝试是在50年前(3)，但最近机器学习的复兴加速了自动识别、测量和表征人眼看不到的模式的努力。例如，通过窦性心律心电图识别阵发性房颤患者，以及根据基线轨迹准确预测年龄和性别(4)。此外，美国食品和药物管理局最近报道了几种用于诊断左心室(LVD)功能不全(LVD)的心电算法(5，6
Since the first report of the human electrocardiogram (ECG) by Augustus Waller in 1887 (1), its diagnostic use has continually expanded. Providing a window to the electric activity of the heart, the ECG allowed the documentation and classification of arrhythmias, soon followed by estimation of atrial and ventricular size (2). Some years later, dynamic changes associated with myocardial infarction were discovered and shown to correlate with clinical and histopathologic changes (2). Researchers have continued to extract a diversifying range of clinically useful data from this simple bedside test, demonstrating the wealth of information contained within it and making the ECG ubiquitous in clinical medicine.Efforts to glean yet more information from the surface electrocardiogram have recently led to the application of machine learning methods to ECG analysis. Although the first attempt to automate ECG analysis was 50 years ago (3), the recent resurgence of machine learning has accelerated efforts to automatically recognize, measure, and characterize patterns not visible to the human eye. Examples include identifying patients with paroxysmal atrial fibrillation from a sinus rhythm ECG, as well as accurately predicting age and sex from a baseline trace (4). Furthermore, several algorithms for ECG diagnosis of left ventricular (LV) dysfunction (LVD) have recently been reported (5, 6), with the US Food and Drug
通过数字电子计算机自动筛查正常和异常心电图。*
DOI：
--
发表时间：
1961
期刊：
Proceedings of the Society for Experimental Biology and Medicine. Society for Experimental Biology and Medicine
影响因子：
--
作者：
H. Pipberger;R. J. Arms;F. Stallmann
通讯作者：
F. Stallmann
影响因子：
14
作者：
Potter, Elizabeth L.;Rodrigues, Carlos H. M.;Marwick, Thomas H.
通讯作者：
Marwick, Thomas H.