Automatic Recognition of Epileptiform EEG Abnormalities Using Machine Learning Approaches

Epilepsy is one of the various neurological disorders with 1% of the world population. It is characterized by the anomalous of a large number of neurons. In this paper, a proposed automated system for seizure detection and diagnosis using EEG signals records. Seizures periods are generally characterized by epileptiform discharges with different changes including spike rate variation according to the shapes, spikes and the amplitude. The epileptiform is used as an indicator to anticipate the EEG signal class using machine learning methods. Based on EEG characterizes the proposed approach achieves a perfect classification rates with 99.8% using the Bonn database.