具有完全连接层的生成对抗网络,以消除PPG信号的噪音
Itzel Alexia Avila Castro1, Helder Oliveira2, Ricardo Goncalves Correia3
1Department of Electrical and Electronic Engineering, University of Nottingham, University Park, Nottingham, NG7 2RD, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
Physiological measurement
|January 13, 2025
概括
这项研究引入了一个完全连接的生成对抗网络 (FC-GAN),以使用运动传感器数据重建损坏的光电显微镜 (PPG) 信号. 该FC-GAN有效地恢复PPG信号,使精确的心率监测,即使有显著的运动文物.
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.
- 运动器件经常破坏PPG信号,限制了它们的临床实用性.
- 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:
- 开发和评估一种用于重建运动损坏的PPG信号的新方法.
- 评估完全连接的生成对抗网络 (FC-GAN) 对PPG信号恢复的有效性.
- 为了确定PPG重建是否可以在不依赖加速度计或陀螺仪数据的情况下实现.
主要方法:
- 一个完全连接的生成对抗网络 (FC-GAN) 架构被设计用于PPG信号重建.
- 来自BIDMC心率数据集的清洁PPG信号被人工损坏,以模拟运动工件.
- 使用这些处理的数据集,FC-GAN模型进行了训练,验证和测试.
主要成果:
- FC-GAN模型在重建扭曲的PPG信号方面表现出显著的有效性.
- 与地面真实心率相比,重建的PPG信号产生了1.31 BPM的平均绝对误差 (MAE).
- 在心率范围为70-115 BPM的范围内评估了表现.
结论:
- 拟议的FC-GAN模型在重建被不同程度的运动工件损坏的PPG信号方面非常强大.
- 该模型在典型的心率范围内的准确性表明其对实时PPG信号处理的潜力.
- 这种方法为PPG重建提供了一个有希望的途径,而不需要辅助运动传感器.
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