Outcome prognostication in comatose patients after cardiac arrest (CA) remains to date a
challenge. The major determinant of clinical outcome is the post-hypoxic/ischemic …

Objective: Most cardiac arrest patients who are successfully resuscitated are initially
comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) …

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

Prognostication for comatose patients after cardiac arrest is a difficult but essential task.
Currently, visual interpretation of electroencephalogram (EEG) is one of the main modality …

Assessing the integrity of neural functions in coma after cardiac arrest remains an open
challenge. Prognostication of coma outcome relies mainly on visual expert scoring of …

Objectives: Visual assessment of the electroencephalogram by experienced clinical
neurophysiologists allows reliable outcome prediction of approximately half of all comatose …

We develop a deep-learning-based algorithm to predict the probability of recovery of a
comatose patient who has suffered a heart attack by analyzing electroencephalogram (EEG) …

Introduction Guidelines recommend use of computerized tomography (CT) and
electroencephalography (EEG) in post-arrest prognostication. Strong associations between …

Electroencephalography (EEG) is increasingly used to assist in outcome prediction for
patients with a postanoxic coma after cardiac arrest. Current literature shows that …

Background To compare three computer-assisted quantitative electroencephalography
(EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest …