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 …

Objective: This paper investigates the hypothesis that focal seizures can be predicted using
scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish …

Recent research has investigated the possibility of predicting epileptic seizures. Intervention
before the onset of seizure manifestations could be envisioned with accurate seizure …

Background Automated seizure detection from clinical EEG data can reduce the diagnosis
time and facilitate targeting treatment for epileptic patients. However, current detection …

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 …

Detecting epileptic seizure from EEG signals constitutes a challenging problem of significant
importance. Combining adaptive optimal kernel time-frequency representation and visibility …

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 …

Epilepsy is a chronic and recurrent brain dysfunction disease. An acute epileptic attack will
interfere with a patient's normal behavior and consciousness, having a great impact on their …

Automated seizure detection from electroencephalography (EEG) would improve the quality
of patient care while reducing medical costs, but achieving reliably high performance across …