Machine learning–based medical anomaly detection is an important problem that has been
extensively studied. Numerous approaches have been proposed across various medical …

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 …

Automated seizure detection and classification from electroencephalography (EEG) can
greatly improve seizure diagnosis and treatment. However, several modeling challenges …

Epilepsy is one of the most prevalent neurological diseases among humans and can lead to
severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to …

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

Epilepsy is a neurological disorder caused by abnormal brain discharges, which can be
diagnosed by electroencephalography (EEG). Although EEG signals are usually easy to …

Multivariate biosignals are prevalent in many medical domains, such as
electroencephalography, polysomnography, and electrocardiography. Modeling …

Seizure type identification is essential for the treatment and management of epileptic
patients. However, it is a difficult process known to be time consuming and labor intensive …

Cross-patient automatic epileptic seizure detection through electroencephalogram (EEG) is
significant for clinical application and research. However, most automatic seizure detection …

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 …