OVAR-BPnet:一种通用脉冲波深度学习方法,用于无袖手套的血压测量
IEEE journal of biomedical and health informatics
|July 4, 2024
概括
这项研究介绍了OVAR-BPnet,这是一种新的深度学习模型,用于使用脉冲波分析进行无袖血压估计. 该模型有效地捕捉了各种数据类型的复杂脉冲波特征,为方便的BP监控铺平了道路.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 心血管健康 心血管健康
背景情况:
- 脉冲波分析提供了一种非侵入性,无袖的方法来测量血压 (BP),这对于精确医学至关重要.
- 目前的脉冲波学习方法在全面的特征提取和概括方面扎,以获得准确的BP估计.
- 对于持续监测BP的强大和多功能深度学习模型的需求是显著的.
研究的目的:
- 为BP估计提出一种通用脉冲波深度学习 (PWDL) 方法,使用新的OVAR-BPnet模型.
- 为了增强复杂的脉冲波特征的捕获,并提高跨多种脉冲波类型的概括性.
- 为了验证PWDL方法在多种光电度学和辐射动脉脉冲压波形上的性能.
主要方法:
- 开发了OVAR-BPnet模型,集成了全尺度卷积,视觉变压器和多层感知子网.
- 雇佣人口脉冲波构造,学习单周期和多周期波形特征.
- 利用数据增强来增强脉冲波形态和标签序列规范化以实现子网络协同作用.
主要成果:
- OVAR-BPnet模型在三个不同的脉冲波类型上表现出卓越的性能:光电流体成像,额头成像光电流体成像和辐射动脉脉冲压波形.
- 在内部评估指标和国际基准中取得了先进的结果,突出显示了强大的竞争力.
- 在不同的脉冲波信号和受试者群体中展示了出色的概括性.
结论:
- 拟议的PWDL方法和OVAR-BPnet模型为无袖血压估计提供了强大而可通用的解决方案.
- 这种方法在便捷和连续的血压监测系统中具有广泛应用的巨大潜力.
- 这项研究代表了深度学习BP估计方法在三个不同的脉冲波模式的首次验证.
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