Long‐term electroencephalography (EEG) monitoring is time‐consuming, and requires
experts to interpret EEG signals to detect seizures in patients. In this paper, we propose a …

Objective: This paper investigates the multivariate oscillatory nature of
electroencephalogram (EEG) signals in adaptive frequency scales for epileptic seizure …

Epileptic seizures are caused by excessive, synchronized activity of large groups of
neurons. In human electroencephalograph (EEG), they are reflected by multiple epileptic …

A novel wavelet-based algorithm for real-time detection of epileptic seizures using scalp
EEG is proposed. In a moving-window analysis, the EEG from each channel is decomposed …

Seizure is a common phenomenon among patients with epilepsy. Detection of seizure at the
onset enables immediate actions to be taken. However, real-time seizure event and onset …

About 1% of the people in the world suffer from epilepsy. The main characteristic of epilepsy
is the recurrent seizures. Careful analysis of the electroencephalogram (EEG) recordings …

Automatic seizure detection is of great significance for epilepsy long-term monitoring,
diagnosis, and rehabilitation, and it is the key to closed-loop brain stimulation. This paper …

In this paper, we propose a novel method using best basis wavelet functions and double
thresholding that are well suited for detecting and localization of important epileptic events …

The electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. Long-
term EEG recordings of an epileptic patient contain a huge amount of EEG data. The …

Goal: The accurate automatic detection of epileptic seizures is very important in long-term
electroencephalogram (EEG) recordings. In this study, the wavelet decomposition and the …