中国组织工程研究

中国组织工程研究 ›› 2011, Vol. 15 ›› Issue (52): 9808-9812.doi: 10.3969/j.issn.1673-8225.2011.52.027

• 数字化骨科 digital orthopedics • 上一篇    下一篇

BP神经网络在急性低血压发生预测模型中的应用

吴剑波，赖丽娟，李宁山，吴效明   

  1. 华南理工大学南校区计算中心，广东省广州市510006
  • 收稿日期:2011-06-08 修回日期:2011-09-13 出版日期:2011-12-24 发布日期:2011-12-24
  • 通讯作者: 吴效明，教授，博士生导师，华南理工大学南校区计算中心，广东省广州市 510006 bmxmwus@scut.edu.cn
  • 作者简介:吴剑波★，男，1978年生，重庆市人，汉族，硕士，主要从事软件技术的研究。 wjbok@scut.edu.cn

Application of back propagation neural networks in the prediction of acute hypotension

Wu Jian-bo, Lai Li-juan, Li Ning-shan, Wu Xiao-ming   

  1. Computing Centre of South Campus, South China University of Technology, Guangzhou 510006, Guangdong Province, China
  • Received:2011-06-08 Revised:2011-09-13 Online:2011-12-24 Published:2011-12-24
  • Contact: Wu Xiao-ming, Professor, Doctoral supervisor, Computing Centre of South Campus, South China University of Technology, Guangzhou 510006, Guangdong Province, China bmxmwus@scut.edu.cn
  • About author:Wu Jian-bo★, Master, Computing Centre of South Campus, South China University of Technology, Guangzhou 510006, Guangdong Province, China wjbok@scut.edu.cn

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463

被引次数

4

摘要：

背景：ICU监护中术后急性低血压并发症的发生严重威胁着患者的生命安全，目前临床上主要依靠医生的经验进行预见性判断。
目的：为实现急性低血压发生的自动检测和提前预报，运用医学信息学理论，探讨一种预测急性低血压发生的模型。
方法：对发生与未发生急性低血压两者间平均动脉压信号进行小波多尺度分解，并选取各层小波系数的统计特征值中位数和最大值作为信号特征参数，提出了基于BP神经网络方法对提取的信号特征参数进行分类预测，并在MATLAB环境下进行仿真实验。
结果与结论：实验结果表明，利用BP神经网络方法对急性低血压发生的预测是可行的。

关键词: 急性低血压, BP神经网络, 小波变换, 特征参数, 预测

Abstract:

BACKGROUND: The post-operation complications of acute hypotensive episode (AHE) in intensive care units seriously endanger the patient’s lives, and it is depended mainly on the expert experience of doctors to treat.
OBJECTIVE: To detect automatically and forecast the occurrence of AHE and to research a model for predicting AHE by medical informatics theory.
METHODS: Mean arterial pressure (MAP) signals of those people who experienced AHE and those who did not experienced AHE were both described on different scales by using wavelet transform, and the median and maximum from the wavelet coefficients were extracted as the parameters of MAP signal. Then back propagation (BP) neural networks method for classifying and predicting the parameters was developed and simulated in MATLAB environment.
RESULTS AND CONCLUSION: The experiment demonstrates that BP neural networks method was practicable for forecasting the occurrence of AHE.

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