通过使用增强的脉冲波分析和机器学习的手指光电解剖学估计大动脉硬度
Henrik Hellqvist1, Mikael Karlsson2, Johan Hoffman3
1Division of Cardiovascular Medicine, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
Frontiers in cardiovascular medicine
|March 26, 2024
概括
一个简短的手指光电脉扫描 (PPG) 注册可以可靠地估计大动脉硬性,这是一个关键的心血管疾病指标. 这种可访问的方法有望改善心血管风险评估,而不需要专门的设备.
科学领域:
- 生物医学工程 生物医学工程
- 心血管生理学心血管生理学
- 医学诊断 医学诊断 医学诊断
背景情况:
- 大动脉硬度在心血管疾病发展中至关重要,但通常需要专门的评估工具.
- 光电心电图 (PPG) 和心电图 (ECG) 在临床和可穿戴环境中广泛使用.
- 开发可访问的方法来评估大动脉硬度对于广泛的心血管风险评估至关重要.
研究的目的:
- 为了研究使用简短的光斑血学 (PPG) 记录的可行性,单独或与单线心电图一起,可靠地估计大动脉硬度.
- 开发和验证机器学习模型,以使用PPG衍生特征预测大动脉硬.
主要方法:
- 一项概念验证研究涉及33名参与者的同时高分辨率PPG,单导电图和手指血压记录.
- 用参考设备 (SphygmoCor,动脉计) 测量了带关节脉冲波速率 (cfPWV) 和大动脉脉冲波速率 (aoPWV).
- 机器学习模型使用136个已确定的和13个新的PPG波形特征进行训练,具有和没有高度规范的脉冲到达时间 (NPAT) 从心电图.
主要成果:
- 基于PPG的模型准确预测了cfPWV和aoPWV,平方根平均值误差分别为0.70m/s和0.52m/s.
- 一个新的PPG特征 (早期波形振幅比) 与cfPWV (r = -0.81) 和aoPWV (r = -0.75) 显示出强烈的相关性.
- 模型性能显示可重复性和一致性与参考设备相比,在纳入NPAT时略有改善.
结论:
- 一个简短的手指PPG注册,利用新的功能和机器学习,可以在没有额外的患者数据的情况下估计大动脉硬.
- 开发的方法提供了可比的可重复性和与已建立的非侵入性参考设备的一致性.
- 这种基于PPG的可访问方法有可能提高心血管风险评估,治疗策略和患者预后.
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