降低光电显微镜信号中的噪声使用卷积断层自编码器与非传统的训练方案
IEEE transactions on bio-medical engineering
|September 8, 2023
概括
这项研究引入了一个卷积无声自编码器 (CDA) 来从光聚光显微镜 (PPG) 信号中移除运动和噪声器件,改善心率准确性,用于心房动 (AF) 和非AF数据.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 可穿戴技术可穿戴技术
背景情况:
- 来自可穿戴设备的光电显微镜 (PPG) 信号容易受到运动和噪声器件 (MNA) 的影响.
- 这些文物损害了PPG衍生的心率测量的准确性,特别是在现实世界中.
- 准确的心率监测对于心脏健康评估至关重要,包括检测心房动 (AF).
研究的目的:
- 开发一个高效的卷积无解自编码器 (CDA) 网络,以减少损坏的AF和非AF PPG数据中的MNA.
- 通过采用一种非传统的训练策略,以节律特定的数据分布来优化CDA模型的两种心律.
- 为了使可靠的心脏健康监测能够从无声化的PPG信号中准确地提取心率.
主要方法:
- 一个卷积无误自编码器 (CDA) 网络被设计用于处理损坏的PPG段.
- 使用优化方案训练CDA模型,优先考虑AF特定模型的AF数据和非AF模型的非AF数据.
- 该网络在两个公共数据集中对48名受试者的25秒PPG段进行了评估,共计10,773个培训和测试段.
主要成果:
- 拟议的CDA方法显著降低了非AF数据的平均心率根平均平方误差 (RMSE) 的45.74%,非AF数据的23%.
- 与替代重建方法相比,该方法在峰值检测方面显示了较低的RMSE和更高的灵敏度和正预测值 (PPV).
- 撤销的PPG细分产生的心率估计比原始损坏的数据更准确.
结论:
- 开发的CDA网络是一个有希望和可靠的方法,用于否认被MNA破坏的PPG信号.
- 在AF检测算法之前实施这种无声化技术可以提高心脏健康监测的准确性.
- 这些发现支持在可穿戴设备中使用这种方法,以进行可靠的,现实生活中的心血管健康评估.
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