Epilepsy Seizure Prediction Model Based on Dual Mode EEG Overlapping Technique Using Neural Network
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—Epilepsy Seizure is a neural malfunction of electrical ions discharging or ex- changing among the neurons. Typically, the cause and purpose of occurrence is untraced as the normal ions charge based communication is uninterruptable. Various researchers have proposed techniques to analyses the seizure occurrence using Electro Encephalogram (EEG). In this paper, a dual mode EEG overlapping algorithm is proposed towards the detection of Epilepsy Seizure (ES) occurrence under an asymptomatic patient’s datasets. The approach trains datasets under normal and abnormal conservative scenarios. The slightest change of fluxes in ions leads to the blank edge formation i.e., Epilepsy Seizure. The process is then evaluated under a neural networking model. The EEG datasets are formulated with a synchronized pattern leading towards authentication of change. The algorithm is validated on 345 clinical samples approved by medical research communities. The algorithm demonstrates the higher order of Epilepsy Seizure prediction and detection. The sample based validated results is 97.32% accuracy rate in detection with a non-fatal ratio of 2.68% of true negative predications.
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