完全卷积神经网络和PPG信号用于动脉血压波形估计
Yongan Zhou1, Zhi Tan1, Yuhong Liu2,3
1School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, People's Republic of China.
Physiological measurement
|July 4, 2023
概括
这项研究引入了ABP-MultiNet3+,一种用于非侵入性血压估计的新型深度学习模型. 该模型准确地预测了动脉血压波形,使其能够持续监测并潜在地减少心血管疾病风险.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心血管监测 心血管监测
背景情况:
- 准确的动脉血压 (ABP) 波形质量对于可靠的血压估计至关重要.
- 目前用于血压监测的方法可能是侵入性的或缺乏连续跟踪能力.
- 预测ABP波形允许估计静脉血压 (SBP),透气血压 (DBP) 和平均动脉压 (MAP).
研究的目的:
- 开发和验证一个深度学习模型 (ABP-MultiNet3+),用于高质量的动脉血压波形预测.
- 为了提高非侵入性血压估计的准确性和可靠性.
- 为了实现持续的血压监测,以便对心血管疾病进行潜在的早期检测和管理.
主要方法:
- 使用一个完全卷积神经网络,MultiResUNet3+,作为ABP-MultiNet3+模型的核心架构.
- 采用卡尔曼过器对光电缩图 (PPG) 信号,将第一和第二顺序的衍生品作为模型输入.
- 实现了综合平均绝对误差 (MAE) 和平均平方误差 (MSE) 损失函数,以确保准确的波形匹配.
主要成果:
- 在MIMIC II数据库上,ABP-MultiNet3+模型实现了MAP (1.88 mmHg),DBP (3.11 mmHg) 和SBP (4.45 mmHg) 的低平均绝对误差.
- 该模型符合AAMI标准,并根据BHS标准实现了DBP和MAP的A级.
- 根据BHS标准,SBP预测达到B级,比现有方法有所改善.
结论:
- 开发的ABP-MultiNet3+算法证明了有效的无袖血压估计.
- 这项技术有望通过移动医疗设备持续监测血压.
- 成功实施可以通过积极的健康管理显著减轻心血管疾病的影响.
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