Epilepsy is one of the most paramount neurological diseases, affecting about 1% of the
world's population. Seizure detection and classification are difficult tasks and are ongoing …

The intelligent recognition of epileptic electro-encephalogram (EEG) signals is a valuable
tool for the epileptic seizure detection. Recent deep learning models fail to fully consider …

Studies on treatment of epilepsy have been actively conducted in multiple avenues, but
there are limitations in improving its efficacy due to between-subject variability in which …

Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data
can enable more precise diagnosis and efficient management of the disease. This task is …

Electroencephalogram (EEG) based seizure subtype classification is very important in
clinical diagnostics. However, manual seizure subtype classification is expensive and time …

Classification of seizure type is a key step in the clinical process for evaluating an individual
who presents with seizures. It determines the course of clinical diagnosis and treatment, and …

Epilepsy affects nearly 1% of the global population, of which two thirds can be treated by anti-
epileptic drugs and a much lower percentage by surgery. Diagnostic procedures for epilepsy …

Epileptic seizures result from disturbances in the electrical activity of the brain, classified as
focal, generalized, or unknown. Failure to correctly classify epileptic seizures may result in …

Background Neonatal seizures are a common occurrence in clinical settings, requiring
immediate attention and detection. Previous studies have proposed using manual feature …

Accurate classification of seizure types plays a crucial role in the treatment and disease
management of epileptic patients. Epileptic seizure types not only impact the choice of drugs …