Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

Automated interictal epileptiform discharge (IED) detection has been widely studied, with
machine learning methods at the forefront in recent years. As computational resources …

Deep learning for automated interictal epileptiform discharge (IED) detection has been
topical with many published papers in recent years. All existing works viewed EEG signals …

The application of deep learning approaches for the detection of interictal epileptiform
discharges is a nascent field, with most studies published in the past 5 years. Although many …

Objective: This study develops new machine learning architectures that are more adept at
detecting interictal epileptiform discharges (IEDs) in scalp EEG. A comparison of results …

Epilepsy is a complex neurological disorder marked by recurrent seizures, often stemming
from abnormal discharge of the brain. Electroencephalogram (EEG) captures temporal and …

Automated diagnosis of epilepsy for differentiating epileptic EEGs without Interictal Epileptic
Discharges (IEDs) from normal EEGs remains a critical challenge in clinical settings. Current …

The application of deep learning approaches for the detection of inter-ictal epileptiform
discharges is a nascent field, with most studies published in the past 5 years. Although many …

Purpose: Despite availability of commercial EEG software for automated epileptiform
detection, validation on real-world EEG datasets is lacking. Performance evaluation of two …

Moving the field forward: detection of epileptiform abnormalities on scalp
electroencephalography using deep learning—clinical Page 1 BRAIN COMMUNICATIONS …