Design of a Dense Layered Network Model for Epileptic Seizures Prediction with Feature Representation

Abstract

Epilepsy is a neurological disorder that influences about 60 million people all over the world. With this, about 30% of the people cannot be cured with surgery or medications. The seizure prediction in the earlier stage helps in disease prevention using therapeutic interventions. Certain studies have sensed that abnormal brain activity is observed before the initiation of seizure which is medically termed as a pre-ictal state. Various investigators intend to predict the baseline for curing the pre-ictal seizure stage; however, an effectual prediction model with higher specificity and sensitivity is still a challenging task. This work concentrates on modelling an efficient dense layered network model (DLNM) for seizure prediction using deep learning (DL) approach. The anticipated framework is composed of pre-processing, feature representation and classification with support vector based layered model (dense layered model). The anticipated model is tested for roughly about 24 subjects from CHBMIT dataset which outcomes in attaining an average accuracy of 96% respectively. The purpose of the research is to make earlier seizure prediction to reduce the mortality rate and the severity of the disease to help the human community suffering from the disease.