Abstract Machine learning (ML) is a fundamental concept in the field of state-of-the-art
artificial intelligence (AI). Over the past two decades, it has evolved rapidly and been …

Identifying a core set of features is one of the most important steps in the development of an
automated seizure detector. In most of the published studies describing features and seizure …

The electroencephalogram (EEG) has been commonly used to identify epileptic seizures,
but identification of seizures from EEG remains a challenging task that requires qualified …

Neonatology has experienced a significant reduction in mortality rates of the preterm
population and critically ill infants over the last few decades. Now, the emphasis is directed …

The aim of this study was to develop methods for detecting the nonstationary periodic
characteristics of neonatal electroencephalographic (EEG) seizures by adapting estimates …

EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation
in the preterm group is particularly challenging; trained experts are scarce and the task of …

Background Neonatal seizures are a common occurrence in clinical settings, requiring
immediate attention and detection. Previous studies have proposed using manual feature …

Seizure detection is a particularly difficult task for neurologists to correctly identify the
Electroencephalography (EEG)-based neonatal seizures in a visual manner. There is a …

Neonatal seizure detection algorithms (SDA) are approaching the benchmark of human
expert annotation. Measures of algorithm generalizability and non-inferiority as well as …

Epileptic seizure is one of the common neurological disorders, and its clinical manifestation
is different from that of the adult as the neonate's brain is not yet fully developed. In clinical …