Author/Authors :
Liu, Shuang Department of Biomedical Engineering - College of Precision Instruments and Optoelectronics Engineering - Tianjin University - Tianjin, China , Guo, Jie Department of Neurology - Tianjin First Center Hospital - Tianjin, China , Meng, Jiayuan Department of Biomedical Engineering - College of Precision Instruments and Optoelectronics Engineering - Tianjin University - Tianjin, China , Wang, Zhijun Department of Neurology - Tianjin First Center Hospital - Tianjin, China , Yao, Yang Department of Neurology - Tianjin First Center Hospital - Tianjin, China , Yang, Jiajia Department of Biomedical Engineering - College of Precision Instruments and Optoelectronics Engineering - Tianjin University - Tianjin, China , Qi, Hongzhi Department of Biomedical Engineering - College of Precision Instruments and Optoelectronics Engineering - Tianjin University - Tianjin, China , Ming, Dong Department of Biomedical Engineering - College of Precision Instruments and Optoelectronics Engineering - Tianjin University - Tianjin, China
Abstract :
Ischemic thalamus stroke has become a serious cardiovascular and cerebral disease in recent years. To date the existing
researches mostly concentrated on the power spectral density (PSD) in several frequency bands. In this paper, we investigated the
nonlinear features of EEG and brain functional connectivity in patients with acute thalamic ischemic stroke and healthy subjects.
Electroencephalography (EEG) in resting condition with eyes closed was recorded for 12 stroke patients and 11 healthy subjects
as control group. Lempel-Ziv complexity (LZC), Sample Entropy (SampEn), and brain network using partial directed coherence
(PDC) were calculated for feature extraction. Results showed that patients had increased mean LZC and SampEn than the controls,
which implied the stroke group has higher EEG complexity. For the brain network, the stroke group displayed a trend of weaker
cortical connectivity, which suggests a functional impairment of information transmission in cortical connections in stroke patients.
These findings suggest that nonlinear analysis and brain network could provide essential information for better understanding the
brain dysfunction in the stroke and assisting monitoring or prognostication of stroke evolution.
Keywords :
EEG , Stroke , Complexity , Connectivity
