Nearly one-third of patients with epilepsy continue to have seizures despite optimal
medication management. Systems employed to detect seizures may have the potential to …

This study proposes a method of automatic detection of epileptic seizure event and onset
using wavelet based features and certain statistical features without wavelet decomposition …

The epilepsies are a heterogeneous group of neurological disorders and syndromes
characterised by recurrent, involuntary, paroxysmal seizure activity, which is often …

Epilepsy is a chronic neurological condition that affects approximately 70 million people
worldwide. Characterised by sudden bursts of excess electricity in the brain, manifesting as …

Over the last decade, the search for a method able to reliably predict seizures hours in
advance has been largely replaced by the more realistic goal of very early detection of …

During the past two decades, epileptic seizure detection and prediction algorithms have
evolved rapidly. However, despite significant performance improvements, their hardware …

Patients are not able to call for help during a generalized tonic-clonic epileptic seizure. Our
objective was to develop a robust generic algorithm for automatic detection of tonic-clonic …

This paper proposes a method for automatic detection of seizure onset. Two statistical
features: skewness and kurtosis with a wavelet based feature: normalized coefficient of …

OBJECTIVE: A clear classification of partial seizures onset features is not yet established.
Complexity and entropy have been very widely used to describe dynamical systems, but a …

This paper presents a supervised classification method to accurately detect epileptic brain
activity in real-time from electroencephalography (EEG) data. The proposed method has …