本文针对电子血压计不能实现血压的无创连续测量等问题,提出一种基于平稳小波变换(Stationary Wavelet Transform,SWT)算法和光电容积传感技术的无创血压测量方法.实验分析了模拟数据库中的26900个脉搏波信号,通过SWT对光电容积脉搏波进行分解,重构第5层高频系数,提取该重构信号的10个特征参数作为神经网络(Artificial Neural Networks,ANN)的矢量输入,脉搏波对应的血压作为ANN的输出进行血压模型的训练,并对模型进行误差分析.实验结果表明,模型的测试误差达到美国医疗器械促进协会制定的标准,通过该方法可实现血压的无创连续测量.%In order to solve the problem of non-invasive continuous measurement of blood pressure in electronic sphygmomanometer, a non-invasive blood pressure measurement method based on stationary wavelet transform (SWT) algorithm and photoplethysmography were proposed. In the experiment, a total of 26900 pulse wave signals from the mimic database were analyzed and subsequently the pulse wave was decomposed by SWT. Furthermore, 10 characteristic parameters of the 5th layer high frequency reconstruction signal were extracted as the input of artificial neural networks (ANN). The blood pressure corresponding to the pulse wave was taken as the output of ANN to train the blood pressure model. The error analysis of the model was carried out. The results indicated that the error of the model met the standards of the American association for the advancement of medical instrumentation. Therefore, this method can be employed in noninvasive continuous measurement of blood pressure.
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