In the past two decades, significant advances have been made on automated
electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number …

Epilepsy affects almost 1% of the population and most of the approximately 20–30% of
patients with refractory epilepsy have one or more seizures per month. Seizure detection …

Background: Classification and localization of focal epileptic seizures provide a proper
diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long …

The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In
this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy …

Epilepsy is the second most common brain disorder after migraine. Automatic detection of
epileptic seizures can considerably improve the patients' quality of life. Current …

Objective: Epileptic seizure prediction based on scalp electroencephalogram (EEG) is of
great significance for improving the quality of life of patients with epilepsy. In recent years, a …

Abstract The World Health Organization (WHO) recently stated that epilepsy affects nearly
65 million people of the world population. Early forecast of the oncoming seizures is of …

Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop
seizure detection and prediction algorithms using machine learning (ML) techniques with …

It is critical to determine whether the brain state of an epilepsy patient is indicative of a
possible seizure onset; thus, appropriate therapy or alarm may be delivered in time …

Objective: Epilepsy is a neurological disorder arising from anomalies of the electrical activity
in the brain, affecting~ 65 million individuals worldwide. Prediction methods, typically based …