This chapter highlights the current approaches in automated neonatal seizure detection and in particular focuses on classifier based methods. Automated detection of neonatal seizures has the potential to greatly improve the outcome of patients in the neonatal intensive care unit. The electroencephalogram (EEG) is the only signal on which 100% of electrographic seizures are visible and thus is c...

Technologies for automated detection of neonatal seizures are gradually moving towards cot-side implementation. The aim of this paper is to present different ways to visualize the output of a neonatal seizure detection system and analyse their influence on performance in a clinical environment. Three different ways to visualize the detector output are considered: a binary output, a probabilisti...

Background: More than 80% of neonatal seizures are completely subclinical and represent a risk factor for neurodevelopmental delays in preterm infants. Amplitude integrated electroencephalography combined with raw video images (video aEEG/EEG) provides real-time monitoring seizure detection.

Neonatal seizures can lead to later life epilepsy and neurobehavioral deficits, and there are no treatments to prevent these sequelae. We showed previously that hypoxia-induced seizures in a neonatal rat model induce rapid phosphorylation of serine-831 (S831) and Serine 845 (S845) sites of the AMPA receptor GluR1 subunit and later neuronal hyperexcitability and epilepsy, suggesting that seizure...

introduction: screening of newborns with seizure, who have curable pathologic brain findings, might be able to improve their final outcome by accelerating treatment intervention. the present study aimed to evaluate the brain ultrasonography findings of newborns hospitalized with complaint of seizure. methods: the present cross-sectional study designed to evaluate brain ultrasonography findings ...