cbPPGGAN:在基于相机的光电质量学中,用于未配对的脉冲波形的通用增强框架
IEEE journal of biomedical and health informatics
|September 11, 2023
概括
这项研究介绍了cbPPGGAN,这是一种基于摄像机的光斑血学 (cbP PG) 的新型框架,可以改善心率和心率变化分析. 这种方法提高了信号质量,即使在运动工件和不同的照明条件下,也提供了准确的结果.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 计算机视觉 计算机视觉
背景情况:
- 以摄像机为基础的光斑质量学 (cbP PG) 使用面部视频非侵入性地测量血液体积变化.
- 传统方法与运动工件和照明变化作斗争,影响心率 (HR) 和心率变化 (HRV) 分析.
- 深度学习模型在照明变化下退化,尽管重建了高质量的脉冲波形.
研究的目的:
- 开发一个强大的cbP PG框架 (cbPPGGAN),结合传统和深度学习方法的优势.
- 为了提高波形忠实性和概括能力,以便准确的HR和HRV分析.
- 为了解决配对训练数据的局限性,使用新的循环一致性损失.
主要方法:
- 拟议的cbPPGGAN框架用于cbP PG信号增强.
- 在培训期间整合未配对和配对数据源.
- 实现一个周期一致性损失的时间频率一致性.
- 利用传统方法的波形作为重建的输入.
主要成果:
- cbPPGGAN显著提高了传统的cbP PG波形质量,跨越不同的照明和数据集.
- 获得的平均绝对误差 (MAE) 为1.34bpm (BH-rPPG) 和1.65bpm (UBFC-rPPG).
- 已证明的平均点击到点击 (AVBB) 误差为27.46 ms (BH-rPPG) 和45.28 ms (UBFC-rPPG).
- 在HR估计和HRV分析中超越了最先进的方法.
结论:
- cbPPGGAN框架有效地提高了cbP PG信号的质量和准确性.
- 拟议的方法使可靠的HR估计和HRV分析在不受约束的环境.
- cbPPGGAN为非接触式生理监测提供了一个有希望的进步.
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