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.
