一个信号处理工具,用于从动脉血压和光多晶体学波形中提取特征
medRxiv : the preprint server for health sciences
|April 1, 2024
概括
我们开发了一种自动工具,可以从动脉血压 (ABP) 和光聚光学 (PPG) 波形中提取特征. 一个来自17327名患者的新PPG功能库有助于非侵入性血压估计研究.
科学领域:
- 生物医学工程 生物医学工程
- 心血管监测 心血管监测
- 信号处理 信号处理
背景情况:
- 动脉血压 (ABP) 和光聚瘤学 (PPG) 波形对于心血管健康监测和医学研究至关重要.
- 提取的波形特征支持诸如血压监测,感觉监测,心脏输出计算和预测高血压/低血压等应用.
研究的目的:
- 开发一个自动信号处理工具,从ABP和PPG波形中进行标准化的特征提取.
- 从大量患者数据集创建一个全面的PPG功能库,用于进一步研究.
- 促进机器学习模型的开发,用于非侵入性血压估计.
主要方法:
- 开发一个用于ABP和PPG波形分析的自动信号处理工具.
- 使用开发的工具在大型外科手术数据集 (17,327名患者) 上生成PPG功能库.
- 利用提取的特征来开发潜在的机器学习模型.
主要成果:
- 成功创建了一个新的,大规模的PPG功能库.
- 展示了用于波形特征提取的标准化方法.
- 该图书馆为探索机器学习中的PPG特性提供了基础,用于非侵入性血压估计.
结论:
- 拟议的自动信号处理工具解决了ABP和PPG波形分析标准化的需求.
- 生成的PPG功能库为开发用于非侵入性血压估计的机器学习模型提供了宝贵的资源.
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