Abstract Machine learning (ML) is increasingly recognized as a useful tool in healthcare
applications, including epilepsy. One of the most important applications of ML in epilepsy is …

Objective Tracking seizures is crucial for epilepsy monitoring and treatment evaluation.
Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may …

The unpredictability of epileptic seizures is one of the most problematic aspects of the field of
epilepsy. Methods or devices capable of detecting seizures minutes before they occur may …

Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop
seizure detection and prediction algorithms using machine learning (ML) techniques with …

With the advancement in artificial intelligence (AI) and machine learning (ML) techniques,
researchers are striving towards employing these techniques for advancing clinical practice …

The objective of this clinical practice guideline (CPG) is to provide recommendations for
healthcare personnel working with patients with epilepsy, on the use of wearable devices for …

Objective. The detection of seizures using wearable devices would improve epilepsy
management, but reliable detection of seizures in an ambulatory environment remains …

Epileptic seizure is one of the most chronic neurological diseases that instantaneously
disrupts the lifestyle of affected individuals. Toward developing novel and efficient …

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 …

Purpose Wearable automated detection devices of focal epileptic seizures are needed to
alert patients and caregivers and to optimize the medical treatment. Heart rate variability …