可通用深度学习用于基于光聚缩图的血压估计- 一个基准研究.
Mohammad Moulaeifard1, Peter H Charlton2, Nils Strodthoff1
1Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
Machine Learning. Health
|September 17, 2025
概括
基于光聚缩图的血压估计的深度学习模型在新数据集上显示了不同的性能. 模型的通用性至关重要,突出了对强有力的分发外测试和域调整策略的需求.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心血管监测 心血管监测
背景情况:
- 摄影致血镜 (PPG) 为血压 (BP) 监测提供了一种无袖替代方案.
- 深度学习 (DL) 模型越来越多地用于从PPG波形推断BP.
- 目前用于BP估计的DL模型通常基于有限的分布式 (ID) 数据进行评估,质疑它们在现实世界中的通用性.
研究的目的:
- 评估基于PPG的BP估计DL模型的分布外 (OOD) 通用化.
- 在PulseDB数据集和外部数据集上对DL模型性能进行比较.
- 调查改善OOD性能的方法,例如域调整.
主要方法:
- 在PulseDB数据集上训练了五个DL模型.
- 在PulseDB.com上对ID性能进行了基准测试.
- 在多个外部数据集上评估了OOD性能.
- 基于样本的域调整被探索以提高概括性.
主要成果:
- 最好的模型 (XResNet1d101) 实现了较低的ID MAE (9.0/5.8 mmHg SBP/DBP与校准).
- 在没有校准的情况下,OOD MAEs在数据集中范围为10.0-18.6 mmHg (SBP) 和5.9-10.3 mmHg (DBP).
- 在OOD数据集上观察到显著的性能下降,受BP分布差异的影响.
结论:
- 以PPG为基础的BP估计的DL模型对具有不同BP分布的数据集具有有限的概括性.
- 特定对象的校准提高了ID性能,但OOD泛化仍然是一个挑战.
- 域调整技术显示出改善模型稳定性和通用性的前景.
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