| 淳新益,郑秀娟,张 畅,张 昀,刘 凯.遗传算法优化 BP 神经网络的非接触式
血压估计方法[J].电子测量与仪器学报,2021,35(7):53-59 |
| 遗传算法优化 BP 神经网络的非接触式
血压估计方法 |
| Non-contact blood pressure estimation method based on genetic algorithmoptimized bp neural network |
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| DOI： |
| 中文关键词: 血压 脉搏波 特征参数 遗传算法 非接触 |
| 英文关键词:blood pressure pulse wave characteristic parameter genetic algorithm non-contact |
| 基金项目:成都市重点研发支撑计划技术创新研发项目（2020 YF05 00056 SN）资助 |
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| 中文摘要: |
| 血压是人体重要的生理参数,能够反应心脏泵血功能、外周血管阻力、血容,对其进行非接触连续测量在日常生活和诸
多应用场合中具有很重要的意义。 从面部视频中获取相关脉搏波信号,然后提取信号中与血压相关性高的特征参数,从而利用
这些参数建立血压估计的神经网络模型,并采用遗传算法对其进行优化。 通过验证得出遗传算法优化 BP 神经网络(GA-BP)
模型估计能力和拟合精度明显提高,且其估计结果满足相应血压测量标准并能实现血压非接触连续估计,其收缩压估计准确率
为 93. 1%,舒张压估计准确率为 96. 6%。 故通过脉搏波特征参数建立 GA-BP 模型是一种有效非接触式血压估计方法。 |
| 英文摘要: |
| Blood pressure is an important physiological parameter of the human body, which can reflect the pumping function of the
heart, peripheral vascular resistance, and blood volume. Non-contact continuous measurement of blood pressure is of great significance
in daily life and many applications. This paper obtains the relevant pulse wave signal from the facial video, and then extracts the
characteristic parameters of the signal that are highly correlated with blood pressure, so as to use these parameters to establish a neural
network model for blood pressure estimation and optimize it by genetic algorithm. Through verification, it is concluded that the genetic
algorithm optimized BP neural network ( GA-BP) model estimation ability and fitting accuracy are significantly improved, and the
estimation results meet the blood pressure measurement standards while realize the non-contact continuous estimation of blood pressure.
The estimated accuracy rate of systolic blood pressure was 93. 1%, and the estimated accuracy rate of diastolic blood pressure was
96. 6%. Therefore, the establishment of GA-BP model by pulse wave characteristic parameters is an effective non-contact estimation
method of blood pressure. |
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