基于多变量符号转移熵的心电信号研究

摘要

复杂度理论已成为研究生理电信号的热点，而符号转移熵是一种反映系统混乱程度的非线性指标。文章在原有多变量转移熵的基础上提出了多变量符号转移熵，对传统时间序列静态划分方法做出了改进，即将时间序列使用动态自适应分割的方式进行符号化。应用该算法对正常人和冠心病患者的心电信号进行分析，在实验中选取最佳的导联对，结果表明该算法能够显著区分正常人和冠心病患者，对原始心电时间序列叠加上高斯噪声后依然可靠有效。%Using complexity theory to study the physiological signals has become a hot spot .The symbolic transfer entropy is a nonlinear system indicator to reflect the degree of chaos , which can be used as a characteristic of physiological signals .The paper adopts multivariable symbols transition entropy based on the multivariable transfer entropy ,to improve traditional time series static partition method using dynamic adaptive segmentation .Using this algorithm to analysis the ECG of people both normal and coronary heart disease patients .In the experiment by selecting the best lead pair to determine the improved algorithm can significantly distinguish between normal subjects and patients with coronary heart disease.And the original sequence is superimposed on the gauss noise result that the algorithm is still reliable and effective .