A real time based wireless wearable EEG device for epilepsy seizure control

Epilepsy is the disease of brain with more than 60 million (approx) cases worldwide. The cure for epilepsy is surgery and medication. Some patients are not cured with surgery and medicine. One third of the cases still remain with unrestrained epilepsy. There is an existing need of constant monitoring for epileptic seizures in this kind of cases. Treatment can be better provided by the doctors if seizure or any atypical brain activity is predicted before its happening or preventive measures can be taken by the patients themselves also if occurrence of the seizure is predicted beforehand. In inter-ictal and postictal periods brain behaves normally. In case of epilepsy, long time EEG recording are essential from days to week. This ultimately reside the patients in the hospital for too many days. Our proposed methodology is on the way to predict the epileptic seizure prior to its occurrence and administer the brain abnormality in real time. There is no such epileptic seizure prediction algorithm using EEG for clinical applications. Our aim is to learn and develop a good epileptic seizure prediction method/algorithm using scalp EEG i.e. noninvasive approach or by using HVR. The proposed pattern detection approach to be used in real time monitoring for patients of epilepsy has great potential and it can be helpful in civilizing the quality of life of the patients. This paper is to review the current state of research and to compare the performance of different algorithms for real-time classification of electroencephalogram signals and the seizure prediction.