ACN-BiLSTM:一种深度学习方法,用于使用多波长PPG融合进行持续的非侵入性血压测量
Mou Cui1, Xuhao Dong1,2, Yan Zhuang3
1School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China.
这项研究引入了一种用于连续血压监测的新方法,使用与ACNN-BiLSTM模型合并的多波长光电显微镜 (PPG),显著提高了非侵入性测量的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 生理监测 生理监测
- 心血管健康 心血管健康
背景情况:
- 非侵入性持续血压 (BP) 测量对于心血管疾病管理至关重要.
- 单波波长光电显微镜 (PPG) 方法在提高BP预测准确性方面存在局限性.
- 目前BP监测技术的临床应用和传播受到限制.
研究的目的:
- 开发和评估一种用于非侵入性连续血压测量的新方法.
- 通过融合多波长PPG信号来提高BP预测的准确性.
- 评估基于注意力的CNN和BiLSTM (ACNN-BiLSTM) 模型对BP预测的性能.
主要方法:
- 提出了一种融合方法,将四波长的PPG信号与ACNN-BiLSTM模型相结合.
- 处理了来自162名志愿者的PPG信号,以评估融合方法.
- 使用MAE,RMSE和AAMI标准,比较了单个波长与多波长聚变的性能.
主要成果:
- 使用多波长PPG融合的ACNN-BiLSTM模型实现了卓越的BP预测准确性.
- 达到平均绝对误差 (MAE) ±根平均二次误差 (RMSE) 的1.67±5.28 mmHg对于缩血压.
- 实现了1.15 ± 2.53 mmHg的MAE ± RMSE,用于腹痛血压.
结论:
- 多波长PPG融合与ACNN-BiLSTM模型相结合,显示出对非侵入性连续BP监测的重大前景.
- 与传统的单波长PPG方法相比,这种方法提供了更好的准确性.
- 开发的模型可以促进更广泛的临床应用和传播先进的血压测量技术.
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