基于物理学的神经网络用于模拟无袖血压估计的生理时间序列
Kaan Sel1, Amirmohammad Mohammadi2, Roderic I Pettigrew3
1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
NPJ digital medicine
|June 9, 2023
概括
基于物理学的神经网络 (PINNs) 能够使用最少的训练数据准确地估计血压. 这种人工智能方法显著减少了可穿戴生理监测的数据需求,推进了精准医学.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 生理监测 生理监测
背景情况:
- 可穿戴设备为精准医学中人工智能驱动的生理监测提供了机会.
- 使用生物阻抗的无袖血压估计需要大量的地面真相数据,这往往很难获得.
- 当前的人工智能模型与生理系统的复杂和个性化性质作斗争.
研究的目的:
- 开发用于生理时间序列分析的物理信息神经网络 (PINN) 模型.
- 使用最小的基准真相数据提取复杂的心血管信息.
- 为了实现精确的无袖血压估计,减少数据依赖.
主要方法:
- 将泰勒对心血管关系的近似纳入神经网络训练中.
- 为生理时间序列数据开发PINN模型.
- 通过使用从生物阻抗数据中无袖血压估计的案例研究来验证框架.
主要成果:
- 达到高相关性对于静缩 (0.90) 和静缩 (0.89) 血压估计.
- 报告的低误差:静缩 (1.3 ± 7.6 mmHg) 和静缩 (0.6 ± 6.4 mmHg). 报告的低误差:静缩 (1.3 ± 7.6 mmHg) 和静缩 (0.6 ± 6.4 mmHg).
- 与最先进的模型相比,平均减少了15的地面真相训练数据的需求.
结论:
- PINNs提供了一种强大的方法,用于人工智能驱动的生理数据解释,使用最小的训练数据.
- 这种方法显著降低了个性化生物医学应用数据收集的负担.
- 该框架显示了开发未来人工智能算法用于普遍生理监测的前景.
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