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

The sudden and apparently unpredictable nature of seizures is one of the most disabling
aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures …

The intelligent recognition of epileptic electro-encephalogram (EEG) signals is a valuable
tool for the epileptic seizure detection. Recent deep learning models fail to fully consider …

In this work, we proposed a versatile signal processing and analysis framework for
Electroencephalogram (EEG). Within this framework the signals were decomposed into the …

The aim of this study is to establish a hybrid model for epileptic seizure detection with
genetic algorithm (GA) and particle swarm optimization (PSO) to determine the optimum …

Prediction of seizures is a difficult problem as the EEG patterns are not wide-sense
stationary and change from seizure to seizure, electrode to electrode, and from patient to …

Neurostimulation is now an established therapy for the treatment of movement disorders,
pain, and epilepsy. While most neurostimulation systems available today provide stimulation …

OBJECTIVE: Research in seizure prediction from intracranial EEG has highlighted the
usefulness of bivariate measures of brainwave synchronization. Spatio-temporal bivariate …

Objective: The present study proposes a new epileptic seizure prediction method through
integrating heart rate variability (HRV) analysis and an anomaly monitoring technique …

This article presents an automated, patient-specific method for the detection of epileptic
seizure onset from noninvasive electroencephalography. We adopt a patient-specific …