可靠的手腕PPG监控通过减轻皮肤传感器接触的贫困情况
Hung Manh Pham1, Matthew Yiwen Ho1, Yiming Zhang1
1School of Computing and Information Systems, Singapore Management University, Singapore, Singapore.
Scientific reports
|December 17, 2025
概括
不理想的皮肤传感器接触会扭曲光电显微镜 (PPG) 信号. 我们的深度学习框架CP-PPG增强了PPG形态,提高了对心率和血压的心血管健康监测精度.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 摄影电磁镜 (PPG) 对于非侵入性生理监测至关重要.
- 运动工件是公认的PPG挑战,但在静止状态下皮肤传感器接触的次优情况往往被忽视.
- 接触不良会降低PPG信号形态,导致重要的波形特征丧失,传感精度降低.
研究的目的:
- 开发一个深度学习框架 (CP-PPG) 来纠正因皮肤传感器接触不足而扭曲的PPG信号.
- 将接触压力扭曲的PPG信号转换为具有理想形态的信号.
- 为了提高下游生理监测任务的性能,使用更正的PPG信号.
主要方法:
- 提出了一个深度学习框架,CP-PPG,包括一个数据处理管道和一个反向训练的深度生成模型.
- 包含一个定制的PPG感知损失功能用于信号转换.
- 通过形态转换评估,对公共数据集的下游生理监测和野生表现评估来验证框架.
主要成果:
- CP-PPG在PPG信号忠实度上显著改善,平均绝对误差减少了40%.
- 在下游任务中取得了实质性的绩效增长:心率 (HR) 的21%,心率变化 (HRV) 的41-46%,呼吸率 (RR) 的6%,血压 (BP) 的4-5%.
- 在所有评估指标和数据集中观察到一致的改进.
结论:
- 解决非最佳皮肤传感器接触对于基于PPG的精确生理监测至关重要.
- CP-PPG框架有效地恢复了PPG信号形态,并提高了心血管健康监测的可靠性.
- 这项工作强调了PPG传感中接触质量的重要性,并为改善信号完整性提供了强大的解决方案.
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