Identifying Epileptic Seizure by Optimized Feature Extraction Process Using the Method of Feature Fusion Technique

Epilepsy is a brain disorder that results in seizures; general seizure suddenly occurring uncontrollable electrical disturbance the brain. These disturbances can lead to changes behaviour, feelings, movements, etc. It highly essential for patients suffering with epilepsy be diagnosed and treated. The normal detection of done using EEG signals which are time consuming. This paper aims at proposing methodology diagnose by use establishing correlation between statistical calculations signals. A various set features applied non-epilepsy dataset. Features such as domain frequency include mean, skewness, variance, kurtosis, standard deviation, approximate entropy, zero crossings, power spectrum signal energy total area, average DWT coefficient, relation human graph features. Further, considering these features, feature fusion optimization aka FFO carried out helps analysing an optimal way further classification. Moreover, its exploring new enhancing distinguish classes. help diagnosis very efficient manner higher accuracy. In this paper, we propose most working system.