基于数据的发现,为模型预测控制提供了心血管系统稀疏动态模型
Siddharth Prabhu1, Srinivas Rangarajan1, Mayuresh Kothare1
1Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA, USA.
Computers in biology and medicine
|October 15, 2023
概括
本研究介绍了一种基于数据的神经刺激 (VNS) 模型,以改善心血管疾病治疗. 该模型增强了闭环控制,优化VNS交付以获得更好的患者结果.
科学领域:
- 生物医学工程 生物医学工程
- 心血管研究研究心血管研究
- 神经科学是一个神经科学.
背景情况:
- 心血管疾病是全球主要的死亡原因.
- 阴道神经刺激 (VNS) 显示出治疗心血管疾病的前景.
- 目前的开放循环VNS由于患者的变化而存在局限性,需要闭环策略.
研究的目的:
- 开发一个数据驱动的动态模型,用于心血管对VNS的反应.
- 使用开发的模型设计用于VNS的闭环控制器.
- 评估模型对优化VNS在心血管疾病治疗中的充分性.
主要方法:
- 利用非线性动态的稀疏识别 (SINDy) 来构建一个动态模型.
- 模拟的患者数据使用机械模型作为真实测量的代理.
- 为封闭循环VNS实施设计了一个模型预测控制 (MPC) 策略.
主要成果:
- 成功构建了一个基于数据的动态模型,用于平均动脉压和心率.
- 发现的模型证明了可解释性和与实验数据的一致性.
- 使用数据驱动模型的闭环MPC控制器实现了有效的设置点跟踪.
结论:
- 数据驱动模型充分捕捉了VNS控制心血管系统的非线性动态.
- 这种方法为个性化和优化VNS治疗提供了一条途径.
- 这些发现支持在设计先进的神经刺激控制器时使用数据驱动模型.
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