遗传算法优化BP神经网络的非接触式血压估计方法

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。

Non-contact blood pressure estimation method based on genetic algorithm optimized bp neural network

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.