使用独特的多模式可穿戴设备和机器学习算法,在没有袖口的情况下预测血压
Chin-To Hsiao1, Sungcheol Hong2, Kimberly L Branan1
1Department of Biomedical Engineering, Texas A&M University, College Station, 77843, Texas, United States.
Computers in biology and medicine
|May 13, 2025
概括
一个具有多个传感器和随机森林回归算法的新可穿戴设备准确地预测了没有袖口的血压. 这项创新为持续监测心血管健康提供了一个实用的解决方案.
科学领域:
- 生物医学工程 生物医学工程
- 心血管健康技术技术 心血管健康技术
- 医疗保健中的机器学习
背景情况:
- 血压升高是心血管疾病 (CVD) 的一个主要风险因素.
- 目前基于袖子的血压监测是不方便的,不舒服的,而且不是连续的.
- 频繁的监测对于预防严重的心血管并发症至关重要.
研究的目的:
- 开发一种多式可穿戴设备,用于准确的无袖血压预测.
- 使用随机森林回归 (RFR) 算法来提高预测准确度.
- 为设备验证建立一个新的人类受试者研究协议.
主要方法:
- 一个多模式的可穿戴设备,集成了两种光聚体学 (PPG) 和两种生物阻抗 (BioZ) 传感器.
- 测量脉冲波沿辐射动脉传播的情况.
- 使用多模态传感器输入的随机森林回归 (RFR) 算法的应用.
主要成果:
- 该RFR模型在无袖血压预测方面取得了高准确性.
- 在数据集中,缩和腹缩血压的平均绝对误差低于3.3mmHg.
- 与单个传感器模型相比,多模态传感器输入显示出更高的准确性.
结论:
- 开发的多模式可穿戴设备和RFR模型显示了强大的,连续的血压监测的潜力.
- 这项技术为长期的心血管健康管理提供了切实可行的解决方案.
- 需要在不同人群中进一步验证,以建立通用无袖血压估计模型.
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