Title :
Cerebral functional connectivity analysis based on scalp EEG in epilepsy patients
Author :
Zhongjiang Sun ; Gang Wang ; Kuo Li ; Zhonglin Zhang ; Gang Bao
Author_Institution :
Sch. of Life Sci. & Technol., Xi´an Jiaotong Univ., Xian, China
Abstract :
The pathological status of epilepsy can be revealed by the cerebral functional connectivity analysis. The majority of investigations in this field focused on the functional magnetic resonance imaging (fMRI). In consideration of the high temporal resolution of electroencephalogram (EEG) signals, a causality analysis based on the partial directed coherence (PDC) algorithms was applied to explore the cerebral functional connectivity of EEG signals from the perspective of the direction and the intensity of information flow. First of all, the multivariate autoregressive (MVAR) model was established for a moving analysis window. The PDC intensity was then calculated. According to the pathological features of epileptic seizure, the outflow information was regarded as the features of the cerebral functional connectivity based on scalp EEG. At last, the brain electrical activity mapping (BEAM) of outflow information was obtained to compare the physiological features corresponding to different moving windows. The results demonstrated that the intensity and the direction of information flow would be changed before and after the epilepsy seizure onset. The intensity of outflow information was obviously enhanced during seizure onsets and the strongest area was focused on seizure onset zone, which conforms to the clinical prediction and has clear physiological significance. The good intuitive nature and accuracy of the experimental results can provide the basis for the subsequent investigations of seizure detection based on the EEG signals.
Keywords :
autoregressive processes; electroencephalography; feature extraction; medical disorders; medical signal processing; signal resolution; skin; PDC intensity; brain electrical activity mapping; causality analysis; cerebral functional connectivity analysis; clinical prediction; electroencephalogram signals; epilepsy patients; epileptic seizure; fMRI; functional magnetic resonance imaging; information flow intensity; moving analysis window; multivariate autoregressive model; outflow information; partial directed coherence algorithms; pathological features; pathological status; scalp EEG; seizure detection; temporal resolution; Brain modeling; Clinical diagnosis; Electroencephalography; Epilepsy; Partial discharges; Pathology; electroencephalograph; epilepsy; outflow information; partial directed coherence;
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2014 7th International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4799-5837-5
DOI :
10.1109/BMEI.2014.7002786