通过单通道PPG估计连续血压趋势的BIGRU关注
Ziyi Liu1, Yiming Zhang2, Congcong Zhou3
1Guangdong Transtek Medical Electronics Co., Ltd., Zhongshan, 52843, People's Republic of China.
Computers in biology and medicine
|December 6, 2023
概括
这项研究引入了一个BiGRU模型,注意使用光聚缩学 (PPG) 信号进行准确,持续的血压 (BP) 监测. 该模型有效地估计BP趋势,即使没有频繁的校准,为可穿戴健康设备提供了一个有希望的解决方案.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 心血管健康 心血管健康
背景情况:
- 摄影致血镜 (PPG) 提供非侵入性生理监测,但传统模型由于忽视了时间依赖性,难以持续估计血压 (BP).
- 现有的机器学习和神经网络模型通常需要频繁校准,限制实时BP监控应用程序.
研究的目的:
- 提出一个新的BiGRU模型,注意使用单通道PPG和人口统计数据进行准确,连续的BP趋势估计.
- 评估模型在不同校准频率的性能及其对BP估计准确性的影响.
主要方法:
- 开发一个双向门式循环单元 (BiGRU) 模型,与注意力机制集成.
- 使用单通道PPG信号和人口信息进行点对点的BP趋势估计.
- 在两个模式中验证模型:非校准和校准,在患有和没有心血管疾病 (CVD) 的受试者中.
主要成果:
- 在校准和非校准模式下,BiGRU模型在估计缩血压 (SBP) 和腹缩血压 (DBP) 中表现出高精度.
- 估计错误在具有/没有心血管疾病的组之间是可比的,表明了强度.
- 非校准模式显示出有希望的准确性,减少了对频繁设备校准的需求.
结论:
- 提出的BiGRU模型与注意力在PPG信号的连续BP估计中实现了高精度.
- 该模型在非校准模式中的有效性表明,可穿戴设备中的实时,长期BP监测具有潜力.
- 这种方法可以显著推进非侵入性心血管健康监测.
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Direct Method
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