Analyzing epileptogenic brain connectivity networks using clinical EEG data

Epileptogenic brain connectivity networks are altered compared to normal ones. Here, we have investigated the properties of epileptogenic networks by applying graph theoretical, statistical and machine learning approaches to the resting state electroencephalography (EEG) recordings obtained from 30 normal volunteers and 51 patients suffering from generalized epilepsy. In the case of epileptic patients, we have found that the brain networks behave like random networks. There is some loss in node connectivity. Hub nodes are more affected during epilepsy. Hence, the epileptogenic networks show less clustering coefficient than normal ones. In addition, we have identified 11 specific regions of brains and ten most significant connections among them as an epileptogenic signature by feature extraction. The ten most significant features are used to classify 81 sample data sets into two classes, i.e., epileptogenic and normal, with 79.01% accuracy. The highly probable eleven regions of human brain according to the positions of electrodes and connections among them may lead to a progress in the clinical treatment of epileptic patients.