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通过光聚光学信号的加权可见度图表来评估脉冲波速度
Juan M Vargas1, Mohamed A Bahloul2, Mohamed M Boularas3
1Université Paris-Saclay, Inria, CIAMS, Gif-sur-Yvette, 91190, France.
Scientific reports
|August 25, 2025
概括
这项研究引入了一种新的数据驱动的方法,使用光聚光学 (PPG) 信号来估计脉冲波速度 (PWV),这是动脉硬和心血管风险的关键指标. 这种方法对非侵入性心血管健康监测具有很高的准确性.
科学领域:
- 生物医学工程
- 心血管生理学
- 在医疗保健中的机器学习
背景情况:
- 脉冲波速度 (PWV) 是动脉硬和心血管风险预测的关键非侵入性生物标志物.
- 准确的PWV估计对于早期发现和治疗动脉样硬化和高血压等心血管疾病至关重要.
- 现有的PWV评估方法可能缺乏可访问性或需要专门的设备.
研究的目的:
- 开发和验证一种新的以数据为导向的方法,用于估计PWV,使用光聚体图 (PPG) 信号.
- 利用有限可穿透加权可见度图 (LPWVGs) 和机器学习进行可靠的PWV量化.
- 提高PWV估计技术的解释性和临床适用性.
主要方法:
- 从PPG波形及其衍生物构建多个LPWVG,采用多种加权策略.
- 包括半经典信号分析 (SCSA) 描述符,频域指标和形态特征在内的综合特征集的提取.
- 训练可解释增强机 (EBM) 模型进行预测建模和可解释性.
主要成果:
- 拟议的基于LPWVG的EBM模型在PWV估计中实现了高准确性和稳定性.
- 阳性测试结果给出了[公式:见文本]的R平方和[公式:见文本]的平均绝对误差.
- 负测试显示[公式:见文本]的平均绝对误差,证实了模型的可靠性.
结论:
- 使用LPWVG和EBM的新型数据驱动方法为非侵入性PWV估计提供了准确和可解释的方法.
- 这项技术显示出临床和门诊心血管健康查,风险分层和衰老研究的巨大潜力.
- 这些发现支持将这种方法纳入常规心血管监测和诊断的可行性.
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