PhysCL:对生理信号模型的知识意识对比学习,用于手套式血压估计
IEEE journal of biomedical and health informatics
|March 25, 2025
概括
生理学知识意识对比学习 (PhysCL) 显著降低了无袖血压估计中标记数据的需求. 这种新的方法提高了使用未标记的光电显微镜 (PPG) 数据的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 生理信号处理 生理信号处理
背景情况:
- 对于无袖式血压估计的深度学习,需要广泛的标记式光电显微镜 (PPG) 数据.
- 由于数据采集的复杂性和成本,当前的方法面临挑战.
研究的目的:
- 引入生理学知识意识对比学习 (PhysCL) 以减少对标记PPG数据的依赖.
- 为了提高无袖血压估计的准确性,使用未标记的生理信号.
主要方法:
- PhysCL使用一个知识意识的增强银行来生成生理上一致的正对.
- 使用对比特征重建方法来改善特征多样性并防止模型崩.
- 该方法在MIMIC III,MIMIC IV和UQVS数据集上使用交叉数据集验证进行了验证.
主要成果:
- 在数据集中,PhysCL的平均平均绝对误差为9.5/5.9 mmHg (心缩/心放).
- 该模型仅使用2%的标记数据和98%的未标记数据进行预训练,并使用5个样本进行个性化.
- 这比最先进的监督方法改善了6.2% (心) 和4.3% (腹).
结论:
- 未标记的生理数据可以有效地增强没有袖口的血压估计模型.
- PhysCL展示了无监督对比学习在分析生理信号方面的潜力.
- 该方法为生物医学应用中的数据稀缺场景提供了实际解决方案.
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