使用机器学习算法通过心律图来确定一个人的COVID后状态
Sergey V Stasenko1, Andrey V Kovalchuk2, Evgeny V Eremin3
1Neurotechnology Department, Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
研究人员开发了一种新方法,使用心电图 (ECG) 数据来检测COVID后的疾病. 这项技术可以识别"心脏",可能标记COVID-19.
科学领域:
- 心脏病学 心脏病学
- 医疗技术 医疗技术 医学技术
- 传染性疾病 传染性疾病
背景情况:
- 后COVID条件影响心脏功能,需要可靠的检测方法.
- 电心电图 (ECG) 数据为心律调节提供了一个非侵入性的窗口.
- 当前的诊断工具可能无法完全捕捉COVID后心脏变化的细微差别.
研究的目的:
- 引入一种新的方法,使用心电图来检测COVID后的疾病.
- 为了识别特定的心电图标记,称为"心脏",表明过去的COVID-19感染.
- 探索这些心脏的潜力,作为COVID特异性心律调节的客观标记.
主要方法:
- 利用卷积神经网络 (CNN) 来分析心电图数据.
- 开发了一种检测算法,用于识别COVID后患者的"心脏".
- 进行了血液参数测量,并为康复的COVID-19患者创建了个人资料.
主要成果:
- 在测试样本中检测心脏的准确率达到了87%.
- 证实心脏是固有的生理信号,而不是硬件工件.
- 在康复患者中,ECG发现与血液参数概况之间建立了相关性.
结论:
- 这种基于CNN的新方法有效地检测心脏,表明COVID后的心脏变化.
- 心脏杆显示出作为可靠的生物标志物对COVID-19对心律的影响有前途.
- 这些发现支持开发用于COVID-19诊断和监测的远程查工具.
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