系统性血压估计方法使用心电图RRI数据
Etsunori Fujita1, Ryuichi Uchikawa2, Yumi Ogura2
1Delta Tooling Co., Ltd, 1-2-10 Yanoshinmachi Aki-ku, Hiroshima City, Hiroshima, 736-0084, Japan. gonzo@deltatooling.co.jp.
Scientific reports
|November 17, 2025
概括
这项研究引入了一种新的混乱时间序列分析,用于控制动脉压力. 来自心电图RRI数据的梯度有效地捕捉了中间压力调节中的混乱变化.
科学领域:
- 身体生理学 身体生理学
- 复杂系统分析 复杂系统分析
- 生物医学工程 生物医学工程
背景情况:
- 血压调节涉及复杂的机制.
- 像Guyton模型这样的现有模型为理解这些系统提供了一个框架.
- 非神经中间压力控制机制起着至关重要的作用.
研究的目的:
- 为非神经介质压力控制提出一种新的混沌时间序列分析方法.
- 调查心电图RRI数据与动脉压力之间的关系.
- 为了识别掌握压力控制混乱变化的参数.
主要方法:
- 使用心电图 RRI 延迟坐标系统来获得中间压力控制机制的响应.
- 使用剩余函数和最佳近似函数确定两个频率范围,以确定动作和补偿.
- 估计的控制对象值使用触点的梯度和点的状态量.
主要成果:
- 开发了一个多项式,创建了一个3D响应表面 (R2=0.814),在225例中将心电图RRI梯度与手臂缩血压相关联.
- 通过对一个受试者的120次心电图RRI读数数据验证了该模型,显示与测量的手臂缩血压有很强的相关性 (R2=0.8564).
- 证明触角梯度是捕捉中间压力控制机制混乱变化的有效参数.
结论:
- 提出的混沌时间序列分析方法对于研究非神经中间压力控制是有效的.
- 来自心电图RRI数据的梯度是理解动脉压调节动态的有价值参数.
- 这种方法为使用混沌理论分析生理控制系统提供了一个新的视角.
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