Analysis of depressive EEG signals based on symbolic phase transfer entropy
Objective Depression is the main cause of disability worldwide and a major contributor to the global burden of diseases. The effects of depression may be long-term or recurrent,and can greatly affect a person's function and ability to live a meaningful life. Depression,characterized by continuous and long-term depression,is the most important type of mental illness in modern people. Nonlinear analysis of EEG data from depression patients and healthy individuals is conducted through symbol phase transfer entropy, exploring the coupling characteristics of depression EEG and inferring the direction and intensity of information flow between systems,providing a new direction for clinical diagnosis of depression patients. Methods By introducing phase into the time series,symbol phase transfer entropy is achieved. Then,the symmetric EEG signals of depression and healthy individuals are windowed and processed to calculate the symbol phase transfer entropy and symbol transfer entropy values. The obtained data are subjected to a t-test,and the experimental results are analyzed and compared with the symbol transfer entropy. Results The symbol phase transfer entropy only requires a small amount of data to obtain a relatively stable transfer entropy value. The statistical results show that there is a significant difference (P<0. 05) between the symbol phase transfer entropy of depression EEG on leads T7 and T8 and healthy EEG. In contrast, the symbol transfer entropy method cannot effectively identify the coupling characteristics of depression EEG. Conclusions In the analysis of left and right symmetric brain region leads, symbol phase transfer entropy is more effective than symbol transfer entropy, and can effectively analyze the coupling characteristics of depressive EEG. The results are helpful for the study of pathological characteristics of depressive EEG.
EEGdepressionsymbolic phase transfer entropysymbolic transfer entropysymbolization
万方数据
维普
