心脏病和中风的生物标志物发现的多元基础
Madhur Mangalam1, Arash Sadri2,3, Junichiro Hayano4
1Division of Biomechanics and Research Development, Department of Biomechanics, and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE, 68182, USA. mmangalam@unomaha.edu.
Scientific reports
|October 25, 2023
概括
这项研究引入了一种新方法,通过解决生物过程中的非ergodicity来创建可靠的数字生物标志物来治疗心脏病和中风等疾病. 新的级联动态描述器提高了生物标志物的特异性和通用性.
科学领域:
- 生物医学工程 生物医学工程
- 复杂系统科学 复杂系统科学
- 数字健康数字健康
背景情况:
- 可靠的生物标志物需要特异性,通用性和可重复性,而这些特征在生物过程中受到非ergodicity的挑战.
- 当前的统计方法假定了ergodicity,即指标在个人和时间之间趋同,这是生物学中经常违反的条件.
- 对心脏病和中风等主要死亡原因缺乏可靠的生物标志物,这凸显了对新方法的需求.
研究的目的:
- 开发一种方法来从非ergodic生物现象中得出可概括的推断.
- 通过解决破坏ergodicity的动态来确定心脏病和中风的可靠数字生物标志物.
- 引入级联动力学作为突破ergodicity的关键因素,并提出克服它的方法.
主要方法:
- 建议捕捉级联动力学作为生物过程中突破ergodicity的起源.
- 通过挑战心脏病和中风生物标志物的识别来评估假设.
- 利用原始的R-R间隔数据,并引入级联动态描述符,包括赫斯特指数和多分位非线性.
主要成果:
- 证明原始的R-R间隔数据和传统的平均值/差异描述符对于心脏病和中风是非ergodic和非特异性的.
- 表明级联动态描述符 (赫斯特指数,多分形非线性) 描述心率变化更为精确,并且是特定的.
- 验证了数字健康生物标志物的拟议级联动态方法的特异性和通用性.
结论:
- 级联动力学是破解生物过程中的ergodicity的关键来源,例如心率变化.
- 拟议的级联动态描述器为数字生物标志物发现提供了更具经验和具体的方法.
- 这项研究开创了对ergodicity原则的应用,以开发可靠的数字健康和疾病生物标志物.
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