基于小波分析和Gaussian回归的急性低血压预测

Acute hypotension prediction based on wavelet analysis and Gaussian regression

急性低血压是危害病人健康的并发症之一,对急性低血压发生的提早预测,能够帮助医生对重症病人找到更好的医疗处理方案。提出了一个基于趋势分量的Gaussian函数拟合预测模型,即用小波多尺度分析提取出信号的趋势分量;再根据Gaussian回归模型对趋势分量进行函数拟合,得到的函数参数作为特征值,用支持向量机SVM对数据分类。Gaussian回归模型使用的是数据驱动,用系数来描述数据之间的关系。通过在较大病人数据集上实验得到了较好的效果。

Acute hypotension is one of the complications which endanger patients＇ lives in ICU. Earlier prediction for acute hypotension can help doctors find better medical treatment options. We propose a prediction model which bases on trend-component based Gaussian function fitting. We first use wavelet multi-scale analysis to extract the trend components of the signals, whose function is fitted based on the Gaussian regression model. The Gaussian regression model is data driven. Its coefficients are used to describe the relationship between data, which are classified by the support vector machines （SVMs）, and the function parameters are used as feature values. Experiments on a large dataset of patients prove that the new algorithm has better prediction results.