基于施罗丁格频谱的连续无袖血压估计,使用PPG信号及其第二个衍生品的临床相关特征
Sayan Sarkar1, Aayushman Ghosh2
1Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Computers in biology and medicine
|October 8, 2023
概括
这项研究引入了一种新的方法,用于使用光聚光学信号和机器学习来估计无袖血压. 该技术实现了高精度,符合医疗标准,并显示出移动健康应用的前景.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 精确的血压 (BP) 监测对于心血管健康管理至关重要.
- 传统的血压测量方法具有侵入性或繁性,需要开发非侵入性技术.
- 光电脉冲显微镜 (PPG) 信号为BP估计提供了一个有希望的非侵入性来源.
研究的目的:
- 开发和验证使用PPG信号的新型无袖血压估计方法.
- 为了提高PPG信号重建,提高BP预测准确度.
- 评估机器学习模型的性能与半经典信号分析 (SCSA) 结合用于BP估计.
主要方法:
- 利用多种机器学习 (ML) 模型和半经典信号分析 (SCSA) 技术,从PPG信号进行无袖的BP估计.
- 开发了一种新的信号重建算法,以优化SCSA并改善准确性-复杂性权衡.
- 从重建的PPG及其第二导数 (SDPPG) 中提取了使用回归算法预测BP的光谱和形态特征.
主要成果:
- 通过使用CatBoost算法,实现了缩血压的平均绝对误差 (MAE) 为5.37 mmHg,缩血压的平均误差为2.96 mmHg.
- 符合医学仪器进步协会的标准,并在英国高血压协会的协议中获得A级.
- 在各种数据集 (in-silico,MIMIC-II,MIMIC-III,昆士兰) 上表现出强大的性能,在噪音条件下 (高达10dB SNR) 保持准确性.
结论:
- 拟议的无袖血压估计技术,集成ML和SCSA,提供高精度,符合临床标准.
- 新的信号重建算法提高了SCSA对BP监测的可靠性和适用性.
- 该方法的简单实施和稳定性使其适用于移动医疗保健设备.
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