Complexity may be one of the most important measurements for analysing time series data;
it covers or is at least closely related to different data characteristics within nonlinear system …

The ultimate goal of epilepsy therapies is to provide seizure control for all patients while
eliminating side effects. Improved specificity of intervention through on-demand approaches …

Objective Prediction of epileptic seizures can improve the living conditions for refractory
epilepsy patients. We aimed to improve sensitivity and specificity of prediction methods, and …

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 …

Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning
system capable of alerting patients prior to seizures to allow the patient to adjust activities or …

Abstract Interpretation of long-term Electroencephalography (EEG) records is a tiresome
task for clinicians. This paper presents an efficient, low cost and novel approach for patient …

Supervised machine learning-based seizure prediction methods consider preictal period as
an important prerequisite parameter during training. However, the exact length of the preictal …

Technological advances are dramatically advancing translational research in Epilepsy.
Neurophysiology, imaging, and metadata are now recorded digitally in most centers …

This study establishes a new data-driven approach to brain functional connectivity networks
using scalp EEG recordings for classifying pediatric subjects with epilepsy from pediatric …

Epilepsy is one of the most common neurological diseases and the most common
neurological chronic disease in childhood. Electroencephalography (EEG) still remains one …