Purpose Automatic seizure detection is significant for the diagnosis of epilepsy and the
reduction of massive workload for reviewing continuous EEG recordings. Methods …

Imbalance data classification is a challenging task in automatic seizure detection from
electroencephalogram (EEG) recordings when the durations of non-seizure periods are …

Background Many investigations based on nonlinear methods have been carried out for the
research of seizure detection. However, some of these nonlinear measures cannot achieve …

Automated seizure detection using EEG has gained increasing attraction in recent years and
appeared more and more helpful in both diagnosis and treatment. How to design an …

Epilepsy is one of the most common neurological disorders–approximately one in every 100
people worldwide are suffering from it. The electroencephalogram (EEG) is the most …

Epilepsy, one of the most common neurological disorders of the human brain, is
unpredictable and irregular. There is much difficulty involved in its detection. Here, a …

Electroencephalography (EEG) is a common tool used for the detection of epileptic seizures.
However, the visual analysis of long-term EEG recordings is characterized by its subjectivity …

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

The electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy.
Substantial data is generated by the EEG recordings of ambulatory recording systems, and …

The automatic detection and classification of epileptic EEG are significant in the evaluation
of patients with epilepsy. This paper presents a new EEG classification approach based on …