Multivariate symbol transfer entropy analysis on epileptic EEG

Epilepsy is caused by abnormal synchronous discharge of neurons in the brain, which is the main basis for the diagnosis of epilepsy. Use of complexity theory to study the epileptic signal has become a hot spot. The symbolic transfer entropy can be used as a characteristic of epilepsy playing an increasingly important role in the study of epilepsy in EEG feature extraction. But symbolic transfer entropy is generally used to measure the dynamic characteristics and directional information between two variables and ignores the interaction between multivariate. In this paper, epileptic EEG signals is analyzed based on multivariate symbol transfer entropy. By choosing the lead signal and the signal length and analyzing the robustness, the method can be used to distinguish between normal and patients with epilepsy. It is proved the algorithm is robust and reliable. The findings will help clinical diagnosis.