Objective This study was undertaken to review the reported performance of noninvasive
wearable devices in detecting epileptic seizures and psychogenic nonepileptic seizures …

Objective: This paper investigates the multivariate oscillatory nature of
electroencephalogram (EEG) signals in adaptive frequency scales for epileptic seizure …

Although several validated seizure detection algorithms are available for convulsive
seizures, detection of nonconvulsive seizures remains challenging. In this phase 2 study, we …

Epilepsy, which is characterized by recurrent spontaneous seizures, has a considerably
negative impact on both the quality and the expectancy of life of the patient. Approximately …

Background: Using machine learning to combine wrist accelerometer (ACM) and
electrodermal activity (EDA) has been shown effective to detect primarily and secondarily …

Existing automatic detection techniques show high sensitivity and moderate specificity, and
detect seizures a relatively long time after onset. High frequency (80–500Hz) activity has …

Epilepsy is one of the most prevalent paroxystic neurological disorders that can dramatically
degrade the quality of life and may even lead to death. Therefore, real-time epilepsy …

Objective Automated seizure detection and alarming could improve quality of life and
potentially prevent sudden, unexpected death in patients with severe epilepsy. As currently …

People with epilepsy need assistance and are at risk of sudden death when having
convulsive seizures (CS). Automated real‐time seizure detection systems can help alert …

Objective To validate an artificial intelligence-based computer algorithm for detection of
epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy …