A reliable algorithm for the timely prediction of epileptic seizures would be a milestone in epilepsy research. Prediction performances have so far been determined using retrospective data assessment, leaving open the question as to whether they prove statistically significant and clinically useful under prospective conditions. To this aim, a Seizure Prediction Competition has been set up. Here...

The number of people diagnosed with epilepsy as a common brain disease accounts for about 1% the world’s total population. Seizure prediction is an important study that can improve lives patients epilepsy, and, in recent years, it has attracted more and attention. In this paper, we propose novel hybrid deep learning model combines Dense Convolutional Network (DenseNet) Long Short-Term Memory (L...

Prediction of EEG signal from past samples is needed for early diagnosis of patients, suffering from frequent epileptic seizure and/or psychotherapeutic treatment of psychiatric patients. This paper compares the performance of various digital filter algorithms to identify the right candidate for application in EEG prediction. The study includes variation of filter order and past sample size, an...

Seizure prediction has attracted a growing attention as one of the most challenging predictive data analysis efforts in order to improve the life of patients living with drug-resistant epilepsy and tonic seizures. Many outstanding works have been reporting great results in providing a sensible indirect (warning systems) or direct (interactive neural-stimulation) control over refractory seizures...

Introduction: Neurological diseases are much often due to our stressed daily life, and epilepsy is considered as a second cause of hospitalization in neurological illness. It about 30% epileptic cases where medicine would not stop or control seizure; hence, surgical intervention required delineate abnormal hyperexcitable cortical tissue. Defining these epileptogenic zones challenge that require...