通过毛细血管补充时间调节增强的非侵入性血压估计PPG信号的调节
Qianheng Yin1, Yixiong Chen2, Lan Lin1
1College of Chemistry and Life Sciences, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
调节毛细血管补充时间 (CRT) 显著改善光斑血学 (PPG) 信号,用于准确的非侵入性连续血压 (CBP) 监测. 这种技术可以提高机器学习模型的性能,从而实现更好的可穿戴健康技术.
科学领域:
- 生物医学工程 生物医学工程
- 生理监测 生理监测
- 医疗保健中的机器学习
背景情况:
- 非侵入性持续血压 (CBP) 估计对于管理心血管健康至关重要.
- 光电显微镜 (PPG) 信号为CBP监控提供了一个有希望的,非侵入性的方法.
- 目前基于PPG的CBP估计方法面临准确性挑战,需要新的方法.
研究的目的:
- 调查毛细血管补充时间 (CRT) 调制对PPG信号质量的影响.
- 用调制的PPG信号来提高非侵入性连续血压 (CBP) 估计的准确性.
- 用CRT调制的PPG数据评估不同机器学习模型 (ResNetCNN,LSTM,变压器) 的性能.
主要方法:
- 在标准化CRT诱导过程中收集了21名健康参与者的PPG数据 (15秒内9N压力).
- 将PPG信号分割成30秒间隔,比较CRT调制和标准数据.
- 验证了三种机器学习模型 (ResNetCNN,LSTM,Transformer) 使用离开一个主题 (LOSO) 和非LOSO交叉验证.
主要成果:
- 在所有测试的机器学习模型中,CRT调制显著提高了准确性.
- ResNetCNN显示了显著的收益,将MAE降低了高达35.6%,MAPE降低了高达40.6%.
- 所有模型都符合医疗器械准确性的AAMI标准 (平均误差<5 mmHg,SD<8 mmHg).
结论:
- CRT调制是一种高度有效的策略,用于提高CBP估计的PPG信号质量.
- 机器学习模型,特别是ResNetCNN,在CRT调制数据方面显示出显著的改进.
- 这种方法具有很大的潜力,可以改进可穿戴的CBP监控设备,特别是在资源有限的环境中.
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