基于GA优化的MIV-BP神经网络连续血压无创监测方法研究

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振动与冲击 ›› 2019, Vol. 38 ›› Issue (9) : 71-79.

论文

谭霞1　季忠1,2　张亚丹1

作者信息 +

Non-invasive continuous blood pressure monitoring method based on GA-MIV-BP neural network model

TAN Xia1, JI Zhong1,2,ZHANG Yadan1

Author information +

文章历史 +

摘要

针对现有的基于脉搏波传导时间法或脉搏波特征参数法的血压测量模型存在的不足，本文提出利用平均影响值（Mean Impact Value，MIV）法从提取的脉搏波传导时间和脉搏波特征参数中优选出对血压值影响较大的参数作为输入量，血压值作为输出量训练BP神经网络模型，然后采用遗传算法（Genetic Algorithm，GA）对个性化参数进行优化，从而建立一种连续血压无创监测模型—GA-MIV-BP神经网络模型。该模型计算血压的结果与实际测量得到的结果进行Bland-Altman一致性分析，表明两者具有很好的一致性，可互换使用，因此该算法对促进无创连续血压监测方法的临床应用具有积极作用。

Abstract

Aiming at the shortcomings of existing blood pressure measurement models based on pulse wave transit time or pulse wave parameters, a non-invasive continuous blood pressure measurement based on GA-MIV-BP neural network model is proposed. The factors that greatly influence blood pressure from the extracted pulse wave transit time and pulse wave parameters are selected by the mean impact value (MIV) method. These factors are used as inputs, and the actual blood pressure values as outputs, to train the BP neural network model. The individual parameters are then optimized using a genetic algorithm (GA) to establish the GA-MIV-BP neural network model. Bland-Altman consistency analysis indicated that the two values were consistent and interchangeable. Therefore, this algorithm is of great significance to promote the clinical application of a non-invasive continuous blood pressure monitoring method.

导出引用

谭霞1　季忠1,2　张亚丹1. 基于GA优化的MIV-BP神经网络连续血压无创监测方法研究[J]. 振动与冲击, 2019, 38(9): 71-79

TAN Xia1, JI Zhong1,2,ZHANG Yadan1. Non-invasive continuous blood pressure monitoring method based on GA-MIV-BP neural network model[J]. Journal of Vibration and Shock, 2019, 38(9): 71-79

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