在COVID-19患者分类中选择特征的遗传算法.
Cosimo Aliani1, Eva Rossi1, Mateusz Soliński2,3
1Department of Information Engineering, University of Florence, 50139 Florence, Italy.
Bioengineering (Basel, Switzerland)
|September 27, 2024
概括
来自PPG信号的心率变化 (HRV) 分析,结合遗传算法和机器学习,可以识别COVID-19患者的微循环变化. 这种方法有效地区分健康人群和轻度COVID-19感染的人群.
科学领域:
- 心血管生理学心血管生理学
- 计算生物学 计算生物学
- 传染病的动态传染病的动态.
背景情况:
- COVID-19 感染带来风险,包括对微循环功能的影响.
- 了解这些微血管变化对于患者管理至关重要.
研究的目的:
- 通过心率变化 (HRV) 分析,研究COVID-19患者的微循环变化.
- 为了确定关键的HRV参数,表明COVID-19相关的微循环功能障碍.
主要方法:
- 在97名受试者 (50名健康者,47名轻度COVID-19患者) 中,分析了从PhotoplethysmoGraphy (PPG) 信号中获得的HRV参数.
- 利用遗传算法进行特征选择和五个机器学习分类器进行歧视.
- 研究了26个HRV参数,以确定感染的重要指标.
主要成果:
- 遗传算法确定了三个重要的HRV参数:平均RR,alpha1和sd2/sd1.
- 使用这三个参数的决策树分类器实现了82%的准确性,86%的特异性和79%的灵敏性.
- 这些发现表明,在区分COVID-19患者时,选定的HRV参数的有效性.
结论:
- 从PPG信号中提取HRV参数,再加上先进的计算方法,有效地识别了COVID-19中的微循环变化.
- 这种方法有助于区分健康个体和轻度COVID-19患者.
- 该研究验证了机器学习和遗传算法的使用,用于分析传染病中的生理信号.
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