基于光GBM的方法用于手腕光电显微镜的信号质量评估
Wang Jun1, Hui Hui1, Yang Handong1
1College of Bioengineering, Chongqing University, Chongqing, 400030, China.
Physical and engineering sciences in medicine
|August 20, 2025
概括
一个新的机器学习算法可以提高基于手腕的光电显微镜 (PPG) 信号质量评估,以实现可靠的心血管监测. 这种方法通过结合可穿戴健康设备的统计和模板匹配功能来提高准确性.
科学领域:
- 生物医学工程
- 信号处理
- 机器学习
背景情况:
- 在心血管健康监测中,手腕光电显微镜 (PPG) 非常重要.
- PPG信号容易受到运动和光效的影响, 损害数据准确性.
- 强有力的信号质量评估对于基于PPG的可靠健康见解至关重要.
研究的目的:
- 开发和评估用于评估基于手腕的PPG信号质量的机器学习算法.
- 使用可穿戴设备提高心血管参数监测的准确性和可靠性.
主要方法:
- 使用了两个数据集 (WCS和LAB),包括数千个基于手腕的PPG细分.
- 通过无声化和正常化预处理信号.
- 提取了11个时间/频率域统计特征和2个模板匹配特征 (欧几里德距离,相关系数).
- 使用LightGBM分类器与一个主体的交叉验证 (LOSO-CV).
主要成果:
- 在LOSO-CV下,LightGBM分类器在两个数据集上实现了高性能.
- WCS数据集结果:准确度=92. 6%,精度=96. 6%,回忆=89. 8%,F1分数=91. 4%,AUC=0. 925.
- 实验室数据集结果:准确度=96. 1%,精度=98. 1%,回忆=95. 2%,F1分数=96. 6%,AUC=0. 941.
结论:
- 拟议的机器学习算法有效地提高了基于手腕的PPG信号质量评估.
- 结合统计和模板匹配功能可以显著提高性能.
- 这种算法为可穿戴心血管健康监测提供了一个强大的工具.
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