Hierarchal Machine Learning Approach to Explore Automatic Seizure Detection in EEG

Abstract

Epilepsy is one of the neurological disorder diseases which is appear the recurrence of unexpected sudden reactions of the brain as epileptic seizures. There are different types of classification related approaches were introduced to extract efficient EEG signal from set related datasets, brain related human data sets. It is often difficult in identification of brain subtle but emergency changes in EEG wave forms by visual inspection based on research area for bio-medical EEG related retinal images. So that in this paper, we propose Hierarchal Machine Learning Approach (HMLA) which is the combination of Grasshopper Optimization Algorithm (GOA) and Support vector machine (SVM) for automatic seizure detection in EEG. It is extracted and evaluated different parameters to train radius bias kernel function classifiers with different notations.  Grasshopper Optimization Algorithm is used explore effective subset of features and then optimal parameters based on SVM for successful classification of EEG. Further improvement of proposed approach, it gives better EEG classification and enhance diagnosis of epilepsy with effective accuracy 90-100 % with comparison of normal data and epileptic brain image data. Experiments of proposed approach give better and efficient results with comparison of existing approaches with respect to different parameters like accuracy, precision, re-call etc.