对于振荡计血压测量的混合深度形态-时间框架
IEEE journal of biomedical and health informatics
|August 31, 2023
概括
这项研究引入了一种新的2D数据表示和深度学习模型,用于使用振荡计设备更准确地测量血压 (BP). 这种先进的方法显著提高了BP估计的准确性,符合国际标准.
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:
- 振荡计血压 (BP) 装置是自动化血压测量的标准,但其准确性可变.
- 目前的算法无法充分利用振荡计脉冲模式中的信息.
- 提高血压测量精度对于各种患者群体和环境至关重要.
研究的目的:
- 开发一种新的2D振荡计数据表示,用于增强BP的表征.
- 应用深度学习来从这个表示中提取与BP相关的信息特征.
- 为了提高自动化BP测量设备的准确性和可靠性.
主要方法:
- 提出了一种用于动脉系统表征的新型2D振荡计数据表示方法.
- 开发了一种混合卷积循环神经网络 (CNN-RNN) 用于特征提取.
- 在来自245个人的三个振荡计数据库上训练并验证了该模型.
主要成果:
- 取得的平均误差为0.08 mmHg (静脉缩) 和0.04 mmHg (腹缩),标准偏差分别为2.4 mmHg和2.2 mmHg.
- 超过现有的最先进的BP估计技术.
- 超过了当前的BP监测器的国际标准,并且有很大的差距.
结论:
- 拟议的2D数据表示和深度学习模型在BP估计方面取得了重大进展.
- 这种方法可以在不同患者群体和情况中进行更强大,更客观的血压监测.
- 这些发现表明,人工智能驱动的改进在非专业的BP测量工具中具有有希望的未来.
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