Neurological conditions are the leading cause of disability and mortality combined,
demanding innovative, scalable, and sustainable solutions. Brain health has become a …

Electroencephalogram (EEG) monitoring and objective seizure identification is an essential
clinical investigation for some patients with epilepsy. Accurate annotation is done through a …

The electroencephalogram (EEG) is a cornerstone tool for the diagnosis, management, and
prognosis of selected patient populations. EEGs offer significant advantages such as high …

Background In patients with cardiac arrest who remain comatose after return of spontaneous
circulation, seizures and other abnormalities on electroencephalogram (EEG) are common …

Objective Automated detection of spikes and seizures has been a subject of research for
several decades now. There have been important advances, yet automated detection in …

In conventional machine learning (ML) approaches applied to electroencephalography
(EEG), this is often a limited focus, isolating specific brain activities occurring across …

Introduction Long-term electroencephalography (EEG) monitoring is advised to patients with
refractory epilepsy who have a failure of anti-seizure medication and therapy. However, its …

This paper proposes an interpretable ensembled seizure detection procedure using
electroencephalography (EEG) data, which integrates data driven features and clinical …

Background: The integration of medical devices with artificial intelligence (AI) software is
rapidly advancing as technology progresses. AI machine learning can be used in …

Purpose: Despite availability of commercial EEG software for automated epileptiform
detection, validation on real-world EEG datasets is lacking. Performance evaluation of two …