非线性格兰杰因果关系在睡眠生理信号分析中的应用

基于非线性格兰杰因果关系分析睡眠生理信号。分别使用多项式核函数、高斯核函数和Sigmoid核函数将低维空间数据映射到高维特征空间,在高维特征空间使用非线性格兰杰因果方法来分析睡眠生理信号。研究结果表明，脑电信号对心电信号的影响比心电信号对脑电信号的影响更为显著，脑电信号对血压信号的影响比血压信号对脑电信号的影响更为显著，血压对心电信号的影响比心电信号对血压信号的影响更为显著,而且睡眠期样本信号间的格兰杰因果关系更为显著。仿真结果验证了睡眠期信号更能客观地反映生理信号的因果关系。

Application of Nonlinear Granger Causality in Analysis of Physiological Signals During Sleep

A method based on the nolinear Granger causality is used to analyze sleep physiological signal. Polynomial kernel function, Gaussian kernel function and sigmoid kernel function are used to map the linear data in low dimensional input space into high dimensional feature space in which linear Granger method can be used to analyse the biomedical signals. The analysis results show that the causal effect of electrocardlogram (ECG) signals to electroencephalogram (EEG) signals, ECG signals to blood pressure signals and blood pressure signals to ECG signals are more significant than that of the opposite direction. In addition, the results of sleep subjects have more significant difference than that of normal subjects. The simulation results validate that the sleep physiological signal reflects the causality more objectively.