在设备上,连续,无袖和基于加速器的血压监测
概括
这项研究介绍了一种使用人工智能和惯性传感器的新型无袖血压计. 它为心血管疾病管理提供准确,连续和非侵入性读数.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心血管监测 心血管监测
背景情况:
- 传统的血压监测依赖于手,限制了连续和实际的使用.
- 非侵入性,无袖的方法对于有效的心血管疾病管理至关重要.
- 现有的无袖技术往往缺乏准确性,个性化或现实世界的适用性.
研究的目的:
- 推出一个智能惯性血压测量装置.
- 通过使用新型传感器融合和人工智能,实现准确,持续和非侵入性血压估计.
- 促进非临床环境中的实际心血管疾病管理.
主要方法:
- 利用脉冲过渡时间和用两个加速度计在常见动脉上的弹性心脏造影.
- 开发了一种轻量级的设备上神经网络,用于估计心压和腹压血压.
- 雇员平台意识的神经架构搜索和转移学习用于模型优化和个性化 (30秒校准).
主要成果:
- 在临床前试验中 (18名患者) 达到<±2 mmHg的静缩和静缩误差.
- 神经网络模型需要 <100 kB 的内存,从而实现设备上的处理.
- 该设备通过智能手机接口无线提供每秒3次血压读数.
结论:
- 开发的无袖血压监测设备是准确的,非侵入性的,可个性化和实用的.
- 该框架支持专家和非专家用户在非临床环境中持续监测血压.
- 智能惯性设备代表了远程心血管健康管理的重大进步.
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