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Feature Extraction of Epilepsy Seizure Using Neural Network

Meenakshi, Dr. R.K Singh

The neural network classifies, extract Features to identify the EEGs according to the percentage distribution of energy features. An area of great interest is the development of device that incorporate algorithms capable of detecting early onset of seizures or even predicting those hours before they occur. This lead time will allow for new types of interventional treatment. In the near future patient’s seizure may be detected and treated well before any physical harm. Epilepsy is one of the most common neurological disorders with a widespread 0.6-0.8% of the world’s population. One-third of the patients achieve sufficient seizure control from medicine and other 8-10% benefit from respective surgery. For the remaining 25% of patients, no sufficient treatment is currently available. There are number of researchers present in literature and still going on regarding automated detection of epileptic seizures. The work proposed by A Subasi [1] in 2005 decomposition of EEG signal applied to artificial neural network and the work proposed by D Najumnissa [2], the features are considered and modified using neural network. Feature extraction is most important for their classification of healthy and unhealthy subjects. Here we are using pattern recognition for feature extraction using some parameters.