来自光电血压图的动脉血压重建的稳健建模
Jiating Pan1,2, Lishi Liang1, Yongbo Liang1
1School of life & Environmental Science, Guilin University of Electronic Technology, Guilin, 541004, China.
Scientific reports
|December 5, 2024
概括
这项研究引入了一种新的深度学习方法,用于将光斑血图 (PPG) 信号转换为动脉血压 (ABP) 波形. 该方法在心血管监测方面显示出卓越的准确性和稳定性.
科学领域:
- 生物医学工程 生物医学工程
- 心血管生理学心血管生理学
- 医疗保健中的机器学习
背景情况:
- 动脉血压 (ABP) 波形是心血管健康的重要指标.
- 光电脉冲图 (PPG) 信号提供了一种非侵入性方法来评估心血管状况.
- 从PPG精确的ABP波形估计对于持续的心血管监测至关重要.
研究的目的:
- 开发一种新的深度学习模型,将PPG信号转换为ABP波形.
- 为了提高PPG到ABP转换的准确性和稳定性.
- 通过不同数据集和条件验证拟议的方法.
主要方法:
- 使用[Formula:看文本]-Net架构作为一个特征提取器.
- 整合了一个Bi-block,用于在编码器特征提取过程中捕获个性化的时间信息.
- 实现了结合损失函数与深度监督,以提高预测准确度.
- 引入总错误指数 (TEI) 进行全面的绩效评估.
主要成果:
- 在UCI (2.48 ± 1.95,1.42 ± 1.42,1.48 ± 1.36 mmHg) 和VitalDB (2.16 ± 1.53,1.12 ± 0.59,1.35 ± 0.84 mmHg) 数据集中,对于静脉血压 (SBP),腹压 (DBP) 和平均动脉压 (MAP) 实现了低平均绝对误差 (MAE) ±标准偏差 (STD).
- 报告的TEI指数值为0.29 ± 0.10 (UCI) 和0.29 ± 0.15 (VitalDB),表示整体性能.
- 在不同的采样频率和设备中证明了稳定性.
结论:
- 拟议的深度学习方法有效地将PPG信号转换为高精度的ABP波形.
- 与心血管监测的现有方法相比,该方法表现出更高的性能.
- 开发的技术是强大的,可以适应现实世界的应用,使用各种PPG数据.
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