标准化技术的比较,用于使用猪模型对个体基线自由估计绝对低血压状态的标准化技术
Tamara P Lambert1, Michael Chan1, Jesus Antonio Sanchez-Perez2
1The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Biosensors
|February 23, 2024
概括
早期发现低血压症至关重要. 这项研究表明,单独的基线自由方法可以使用可穿戴传感器准确预测绝对血液体积状态 (ABVS),消除了对先前存在的患者数据的需求.
科学领域:
- 生物医学工程 生物医学工程
- 生理学 生理学 生理学
- 可穿戴技术可穿戴技术
背景情况:
- 低血压休克是导致军人死亡的主要原因.
- 目前对低血压的生命体征评估是不可靠的,检测太晚了.
- 需要实时传感方法来早期检测低血压.
研究的目的:
- 开发和评估个人基线无正常化技术,以预测绝对血液体积状态 (ABVS).
- 为了解决以前的模型的局限性,需要用于可穿戴传感器应用的个别基线数据.
主要方法:
- 检查了ABVS预测的七种个人基线无正常化技术.
- 利用一个随机森林模型与来自猪模型的非侵入性可穿戴传感器数据.
- 通过使用性能指标 (R2,AUC,RMSE) 对比正常化方法与没有正常化的方法.
主要成果:
- 特定特征的全球平均值规范化和外部数据集规范化显示了与没有规范化相比的性能.
- 规范化:R2=0.82±0.025,AUC=0.86±5.5×10−3,RMSE=28.30±0.63%. 这是一个非常好的方法.
- 没有正常化:R2 = 0.81 ± 0.045,AUC = 0.86 ± 8.9 × 10−3,RMSE = 28.89 ± 0.84%. 没有正常化:R2 = 0.81 ± 0.045,AUC = 0.86 ± 8.9 × 10−3,RMSE = 28.89 ± 0.84%.
结论:
- 从可穿戴传感器数据中准确预测ABVS可能不需要规范化.
- 这项研究为在重症监护机构的个人基线无ABVS预测提供了基础.
- 实现实时,非侵入性监测,用于早期检测低血压.
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