Abstract

Background: Epileptic Seizure (ES) is a neural disorder that generates an uncontrolled brain signal impulse. The disorder is seen in young children and adults with a positive medical history.

Method: In this paper, a novel approach to epileptic seizure detection and prediction is proposed and evaluated. The seizure is retrained from Electroencephalography (EEG) high-dimension datasets. The EEG datasets further segment features of interdependent EEG into a matrix. This matrix is linked to providing a validation occurrence of similar feature events with a minimum redundancy maximum relevance (MRMR) approach for ES feature optimization.

Result and Discussion: The uncertainty-based genetic algorithm for parametric evaluation and validation (GAPEr) is used for predictive analysis and decision support via a dedicated neural networking model. The sizer detection and prediction are supported and validated via a series of interactions from trained datasets.

Conclusion: The proposed setup has achieved higher accuracy and dependency in decision support of Epileptic Seizure identification and classification based on predictive evaluation.