基于改进的符号转移熵的心脑电信号耦合研究

摘要

Exploration of the coupling relationship in dynamical system has always been a hot topic of many scholars at home and abroad, the traditional symbolic dynamics analysis method may lead to the results from the serious effect of non-stationary time series. This paper employs coarse graining extraction based on research of original transfer entropy. Through theoretical and experimental analysis, we find that the results of transfer entropy have different distribution trend under different extraction conditions in the coupling analysis of electroencephalogram and electrocardiogram. We choose the best effect of signal data extraction method and apply it to the later application analysis. Furthermore, this paper proposes improvement on the method of time series symbolization, using dynamic adaptive segmentation method. The experimental results show that the whether waking period or sleeping stage, coupling between electroencephalogram and electrocardiogram is more significant when using improved symbolic transfer entropy algorithm. It is also better to capture the dynamic information of the signal and the change of complexity of system dynamics, which is more conductive to clinical testing in practical application and has a better effect on the analysis of non-stationary time series.%试图探究动力系统中的耦合关系一直以来都是国内外众多学者关注的热点，传统的时间序列符号化分析方法会使研究结果受序列非平稳性的严重影响，本文在原有转移熵的研究基础上，应用粗粒化提取，经过理论与实验的分析，发现心脑电信号耦合研究中的转移熵值在不同提取情况下对应不同的分布趋势，并选择效果最好的信号数据提取方法用在其后的应用分析中。此外，对时间序列符号化方法提出改进，采用动态的自适应分割方法。实验结果表明，无论清醒期还是睡眠期，改进的符号转移熵算法观测分析到的心脑电信号耦合作用更显著，能更好的捕捉到信号中的动态信息、系统动力学复杂性的改变，更利于医学临床实践应用中的检测，在分析非平稳的时间序列上具有更好的效果。