About 80% of patients resuscitated from CA are comatose at ICU admission and nearly 50%
of survivors are still unawake at 72 h. Predicting neurological outcome of these patients is …

Quantitative electroencephalography (qEEG) refers to the numerical analysis and/or visual
transformations of raw electroencephalography (EEG) signals. Evaluation of qEEG in …

Objective Electroencephalography (EEG) is an important tool for neurological outcome
prediction after cardiac arrest. However, the complexity of continuous EEG data limits timely …

OBJECTIVES: To develop the International Cardiac Arrest Research (I-CARE), a
harmonized multicenter clinical and electroencephalography database for acute hypoxic …

Extracorporeal membrane oxygenation (ECMO), a life-saving technique for patients with
severe respiratory and cardiac diseases, is being increasingly utilized worldwide …

Seizures, Status Epilepticus, and Continuous EEG in the Inte... : CONTINUUM: Lifelong Learning in
Neurology Account Register Activate Subscription Help Subscribe American Academy of …

In recent years, the volume of digitalized web-based information utilizing modern computer-
based technology for data storage, processing, and analysis has grown rapidly. Humans …

The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for
significant strides in patient diagnosis, treatment, and prognostication in neurocritical care …

Aim of the study EEG slow wave activity (SWA) has shown prognostic potential in post-
resuscitation care. In this prospective study, we investigated the accuracy of continuously …

Aim of the review: The primary aim of this systematic review was to investigate the most
common electroencephalogram (EEG)-based machine learning (ML) model with the highest …