Processing EEG signals for Clinical Interpretation in Seizure-Suspected Patients

Epilepsy is one of the most common neurological disorders, affecting around 1 in 200 of the population. However, identifying epilepsy can be difficult because seizures tend to be relatively infrequent events and an electroencephalogram (EEG) does not always show abnormalities. The aim of this project is to develop a new methods that could improve the diagnosis of epilepsy, leading to earlier treatment and to a better quality of life for epileptic patients. The above methods must be composed with a flexible hardware development in order to discriminate noise and had signals from correct EEG, MEG (Magnetoencephalogram) and Eye Image recognition. Even if there are EEG signal classifiers, it is suitable to perform a correct signal processing according to particular clinical reference, that is, it is difficult to have a classifier for all circumstances but it is possible to adapt EEG processing on current patient. This paper deals with a new approach of developing an architecture with a an embedded coding included in a framework agreement between University and IRCCS "E. Medea".