|本期目录/Table of Contents|

[1]刘建,李昱旻,李建清,等.一种基于高斯拟合优化的血压判定算法研究*[J].生物医学工程研究,2022,02:101-106.
 LIU Jian,LI Yumin,LI Jianqing,et al.Research on a blood pressure determination algorithm based on Gaussian fitting optimization[J].Journal of Biomedical Engineering Research,2022,02:101-106.
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一种基于高斯拟合优化的血压判定算法研究*(PDF)

《生物医学工程研究》[ISSN:1006-6977/CN:61-1281/TN]

期数:
2022年02期
页码:
101-106
栏目:
出版日期:
2022-06-25

文章信息/Info

Title:
Research on a blood pressure determination algorithm based on Gaussian fitting optimization
文章编号:
1672-6278 (2022)02-0101-06
作者:
刘建李昱旻李建清刘澄玉 △
东南大学仪器科学与工程学院,南京 210096
Author(s):
LIU Jian LI Yumin LI Jianqing LIU Chengyu
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
关键词:
脉搏波高斯拟合血压测量收缩压舒张压相关性分析
Keywords:
Pulse wave Gaussian fitting Blood pressure measurement Systolic blood pressure Diastolic blood pressure Correlation analysis
分类号:
R318
DOI:
10.19529/j.cnki.1672-6278.2022.02.01
文献标识码:
A
摘要:
为了提高血压测量的准确性和可靠性,本研究提出了一种基于高斯拟合优化和变幅值系数的血压判定算法。首先将提取的振荡波进行小波阈值去噪,并检测其波峰与波谷,去除原信号中的趋势信号,得到所需波形。本算法对高斯拟合进行优化,结合改进的变幅值系数法,最终得到收缩压和舒张压。将结果与欧姆龙血压计的血压值对比,其平均值和标准差都符合AAMI/ISO的标准,两者的相关系数R2为0.985,具有很强的相关性。本研究为基于示波法的血压计算提供了一种新的解决思路,具有较大的参考价值。
Abstract:
To improve the accuracy and reliability of blood pressure measurement, we proposed a blood pressure determination algorithm based on Gaussian fitting optimization and variable amplitude coefficients. The extracted waves were subjected to wavelet threshold denoising and their peaks and troughs were detected to remove the trend signal from the original signal and obtain the desired waveform. The Gaussian fit was optimized and combined with a modified variable amplitude coefficient method to obtain the final systolic and diastolic blood pressures. The results were compared with the Omron sphygmomanometer and the means and standard deviations met the AAMI/ISO criteria, with a strong correlation coefficient R2? of 0.985. It is a new solution for blood pressure calculation based on the oscillometric method, which has great reference value.

参考文献/References

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备注/Memo

备注/Memo:
(收稿日期:2022-03-23)国家自然科学基金资助项目(62171123, 62071241, 81871444);江苏省自然科学基金杰出青年基金资助项目(BK20190014)。△通信作者Email: chengyu@seu.edu.cn
更新日期/Last Update: 2022-07-21