支持人工智能的表皮电子系统,通过智能手机自动监测高血压的预后参数
Milad Eyvazi Hesar1, Niloofar Sadat Seyedsadrkhani1, Dibyendu Khan1
1Institute of Materials in Electrical Engineering 1, RWTH Aachen University, 52074, Aachen, Germany.
Biosensors & bioelectronics
|September 27, 2023
概括
一个新的可穿戴电子系统监测心电图 (ECG) 和心震图 (SCG),以持续评估高血压的关键预后参数. 这款支持智能手机的设备在预先查心肺高血压方面表现有前途.
科学领域:
- 生物医学工程 生物医学工程
- 心血管研究研究心血管研究
- 可穿戴技术可穿戴技术
背景情况:
- 高血压,特别是心肺高血压,对医疗保健造成了重大负担.
- 持续监测预后参数对于早期检测和管理至关重要.
- 现有的监控方法可能具有侵入性,或缺乏持续的实时数据采集.
研究的目的:
- 开发和验证可穿戴,灵活,无线和智能手机支持的表皮电子系统 (EES),用于持续监测高血压的预后参数.
- 将心电图 (ECG) 和心震图 (SCG) 监测整合到一个单一的非侵入性设备中.
- 利用机器学习来分析心电图和脑电图信号,以提取韦斯勒索引.
主要方法:
- 开发了一种使用洁净室技术的薄型轻型EES,集成ECG电极和压电聚合物振动传感器.
- 使用智能手机近场通信 (NFC) 技术为EES提供动力.
- 在公共心电图和记录的SCG数据上开发和训练了一种机器学习算法,以便在智能手机上自动注释峰值并计算韦斯勒指数.
主要成果:
- EES成功地和同步地监测了来自胸部的ECG和SCG信号.
- 机器学习算法准确地提取了特征特征,并计算了韦斯勒指数.
- 临床实验表明,心肺高血压患者的预后表现有希望.
结论:
- 提出的EES技术为持续高血压监测提供了一种新的非侵入性方法.
- 这种系统有可能对心肺高血压进行有效的预先查.
- 智能手机集成允许实时分析,并可以减少医疗保健负担.
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