GloGen:PPG提示在血压估计中的几次射击转移学习
Taero Kim1, Hyeonjeong Lee2, Minseong Kim2
1Department of Statistics and Data Science, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, 03722, Seoul, Republic of Korea.
Computers in biology and medicine
|October 9, 2024
概括
这项研究引入了GloGen,这是一个新的几次射击学习框架,用于从光聚血图 (PPG) 信号中准确估计血压 (BP). GloGen 改进了非侵入性血压监测,特别是对于具有有限数据的高风险群体.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
- 信号处理 信号处理
背景情况:
- 非侵入性健康监测正在通过机器学习取得进展.
- 光电缩图 (PPG) 信号提供了持续血压 (BP) 估计的潜力.
- 目前的模型缺乏对低血压和高血压的概括,这对于高风险群体至关重要.
研究的目的:
- 开发一个强大的几次射击转移学习框架,以使用PPG信号改进BP估计.
- 提高BP估计模型的准确性和概括性,特别是对于代表性不足的BP群体.
- 为了应对在训练数据有限的场景中精确的BP预测的挑战.
主要方法:
- 拟议的全球快速和快速发电机 (GloGen) 框架.
- 采用双提示学习方法:共享功能的全球提示 (GP) 和个性化信号的实例智能提示 (IP).
- 引入了变异罚款 (VP),以确保快速多样化并增强模型的稳定性.
主要成果:
- 在BP估计准确性和稳定性方面,GloGen显著优于传统方法.
- 在基准数据集上表现出卓越的表现,特别是在代表性不足的血压群体中.
- 即使在有限的培训数据下,它也表现出了有效性,突出了其短暂的学习能力.
结论:
- GloGen提供了一种高效,强大的解决方案,用于实时,非侵入性血压估计.
- 该框架显示了医疗保健应用的巨大潜力,需要精确监测稀缺数据的多样化人口.
- 使用双提示和差异处罚的少量射击转移学习对于改进基于PPG的BP估计是有效的.
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