具有完全连接层的生成对抗网络,以消除PPG信号的噪音
Itzel A Avila Castro1, Helder P Oliveira2,3, Ricardo Correia1
1Optics and Photonics Group and Centre for Healthcare Technologies, University of Nottingham, Nottingham, United Kingdom.
Physiological measurement
|January 17, 2025
概括
这项研究引入了一个生成对抗网络来重建损坏的光电显微镜 (PPG) 信号,实现准确的心率估计. 该模型有效地恢复杂的PPG数据,为实时应用提供了有前途的解决方案.
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:
- 光电显微镜 (PPG) 信号对于非侵入性生理监测至关重要.
- 运动工件经常会扭曲PPG信号,限制它们的临床实用性.
- 现有的PPG重建方法通常依赖于额外的传感器数据.
研究的目的:
- 开发和评估一个生成对抗网络 (GAN),用于重建运动损坏的PPG信号.
- 在不使用运动传感器数据的情况下,评估基于GAN的重建方法的性能.
- 从重建的PPG信号来确定心率估计的准确性.
主要方法:
- 一个具有完全连接层的生成对抗网络 (GAN) 设计用于PPG信号重建.
- 来自BIDMC心率数据集的清洁PPG信号被人工破坏,以创建训练和测试数据.
- 该模型使用来自MIMIC II波形数据库的处理数据进行训练和验证.
主要成果:
- 拟议的GAN模型在70-115bpm范围内的信号的心率估计中实现了1.31bpm的平均绝对误差.
- 该模型在重建PPG信号方面表现出有效性,不管引入的腐败的长度和幅度如何.
- 从重建的PPG信号中实现了精确的心率提取.
结论:
- 开发的GAN架构是有效的重建噪音PPG信号,即使有显著的运动文物.
- 该模型显示了实时PPG信号处理的希望,而不需要加速度计或陀螺仪输入.
- 这种方法提供了一个强大的方法来提高基于PPG的健康监测的可靠性.
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