Abstract While Deep Learning (DL) is often considered the state-of-the art for Artificial Intel-
ligence-based medical decision support, it remains sparsely implemented in clinical practice …

Background: Diagnosing epileptic seizures using electroencephalogram (EEG) in
combination with deep learning computational methods has received much attention in …

Electroencephalography is the recording of brain electrical activities that can be used to
diagnose brain seizure disorders. By identifying brain activity patterns and their …

Epilepsy is a neurological disorder that causes unusual behavior, sensations, and, in some
cases, loss of awareness. It is accompanied by seizures, which are intervals of unusual …

Detecting brain disorders using deep learning methods has received much hype during the
last few years. Increased depth leads to more computational efficiency, accuracy, and …

Background Epilepsy is one of the most common neurological conditions globally, and the
fourth most common in the United States. Recurrent non-provoked seizures characterize it …

Advances in deep learning methods present new opportunities for fixing complex problems
for an end to end learning. In terms of optimal design, seizure detection from EEG data has …

Epileptic seizure detection and classification in clinical electroencephalogram data still is a
challenge, and only low sensitivity with a high rate of false positives has been achieved with …

Epilepsy is a chronic neurological disease characterized by a large electrical explosion that
is excessive and uncontrolled, as defined by the world health organization. It is an anomaly …

In this work, we investigate epilepsy seizure detection using machine learning. In the
literature, a machine learning model is usually trained to help automate the epileptic …