在马尔科夫决策过程框架中使用双重深度强化学习的自适应心跳调节.
Walid Ayadi1, Emad Alkhazraji2, Haitham Khaled1
1Mechatronics and Intelligent Systems, Abu Dhabi Polytechnic, Abu Dhabi, United Arab Emirates.
Scientific reports
|October 9, 2025
概括
这项研究引入了具有双重深度强化学习 (DDRL) 的自适应非线性干扰补偿器 (ANDC),以稳定心律. 这种新的方法在各种条件下有效地管理心脏活动,优于传统方法.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 控制系统 控制系统
背景情况:
- 心律不规则会导致严重的健康问题.
- 稳定人类心跳是研究的一个重要领域.
- 现有的方法可能无法充分解决复杂的心脏动态.
研究的目的:
- 开发和评估一种新的控制策略,以稳定心脏活动.
- 提高心律管理的适应性和稳定性.
- 研究一种结合自适应非线性干扰补偿器和深度强化学习方法的有效性.
主要方法:
- 开发了一个自适应的非线性干扰补偿器 (ANDC) 策略.
- 采用双重深度强化学习 (DDRL) 算法进行自适应控制器校准.
- 使用马尔科夫决策过程 (MDP) 框架构建了一个动态心脏模型.
- 实现了一个闭环系统,用于稳定性和干扰减轻的ANDC,以及用于参数改进的DDRL.
- 使用正常和随机信号评估性能,并使用多个频率组件模拟病态神经活动.
主要成果:
- 根据ANDC-DDRL的策略,可以有效地稳定心律.
- 该系统在正常,不确定的 (随机信号) 和病理条件下显示出稳健性.
- 定量评估 (峰值振幅,信号能量,零穿越率) 证实了心脏模型状态的改善.
- 拟议的方法在稳定心脏活动方面优于传统的基线技术.
结论:
- ANDC-DDRL策略为稳定心律不稳定提供了有效的解决方案.
- 与传统方法相比,这种自适应控制方法提供了更好的性能.
- 这些发现支持先进的控制算法在管理心血管病理方面的潜力.
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