DNN-BP:使用深度学习模型从最佳PPG特征进行无袖血压测量的新框架
S M Taslim Uddin Raju1, Safin Ahmed Dipto2, Md Imran Hossain2
1Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh. taslimuddinraju7864@gmail.com.
这项研究引入了一种使用深度神经网络和光多聚光学 (PPG) 信号进行连续血压 (BP) 监测的新算法. 该方法准确地估计了缩和腹缩血压,符合医疗标准,并为移动医疗设备提供了潜力.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 持续的血压 (BP) 监测对于健康管理至关重要,但由于不舒服的袖子设备而受到阻碍.
- 光电脉冲图 (PPG) 信号为血压监测提供了一个非侵入性的替代方案.
- 现有的基于PPG的BP估计方法需要提高准确性和可靠性.
研究的目的:
- 开发和验证一种用于连续BP监测的新型算法,仅使用PPG信号和深度神经网络 (DNN).
- 通过使用先进的信号处理和整体特征选择技术来提高BP估计的准确性.
主要方法:
- 来自125名受试者 (218条记录) 的PPG信号被获取并处理以消除噪音和文物.
- 对PPG信号进行脉冲波分析,以提取域特定特征.
- 一种集合特征选择方法结合了四个子集,以确定基于DNN的BP预测的最佳特征.
主要成果:
- 拟议的DNN算法与整体特征选择实现了高准确度估计静缩血压 (SBP) (R2=0.962,MAE=2.480 mmHg) 和静缩血压 (DBP) (R2=0.955,MAE=1.499 mmHg).
- 该算法满足了SBP和DBP估计的医疗仪器标准的进步.
- 根据英国高血压协会对SBP和DBP的标准,结果达到A级.
结论:
- 使用具有最佳特征集和DNN模型的PPG信号,可以准确估计连续BP.
- 开发的算法显示了将其集成到移动医疗设备中的巨大潜力,以方便地监测血压.
- 这种非侵入性方法为传统的手式血压测量设备提供了有希望的替代方案.
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