EPILEPSY SEIZURE DETECTION USING EEG SIGNAL

Abstract

Epilepsy is one of the most common neurological diseases that affects millions of persons all over the world. The disease has always been of great importance in the biomedical field, due to the health risks it causes. It is characterized by recurrent, unprovoked seizures and can be assessed by the electroencephalogram (EEG). Electroencephalogram (EEG) is one of the most powerful tools that offer valuable information related to different abnormalities in the human brain. One of these abnormalities is the epileptic seizure.EEG measures the electrical activity in the brain, and one important aspect of the epilepsy research includes analyzing the EEG data in order to detect epileptic seizures in early stages. A framework is proposed for detecting epileptic seizures from EEG signals recorded from normal

and epileptic patients. The suggested approach is designed to classify the abnor- mal signal from the normal one automatically. This work aims to improve the

accuracy of epileptic seizure detection and reduce computational costs.To address this, the proposed framework uses five machine learning (ML) classifiers:Logistic Regression,Decision Trees,Random Forest Gradient Boosting classifier,Extremely Random Trees.However, the Extremely Random Trees classifier achieved the best accuracy and it outperformed the other examined classifiers.