In an intensive care unit (ICU), an accurate prognosis of comatose patients' recovery is
critical for ongoing medical interventions. Patient prognosis guides decisions around …

In this work, we describe the creation of our machine-learning-based solution for coma
prognosis after cardiac arrest using longitudinal EEG and ECG recordings for the “Predicting …

Patients resuscitated from cardiac arrest who enter a coma are at high risk of death.
Forecasting neurological outcomes of these patients (ie, the task of neurological …

As part of the 'Predicting Neurological Recovery from Como After Cardiac Arrest: The
George B. Moody PhysioNet Challenge 2023', we propose a two-step approach, which …

Aims: Interpretation of continuous EEG is a demanding task that requires the expertise of
trained neurologists. However, these experts are not always available in many medical …

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) …

Many survivors of cardiac arrest subsequently end up in a coma state, and these patients
will go onto achieve varying levels of neurological recovery, ranging from brain death to full …

The George B. Moody PhysioNet Challenge 2023 invited teams to develop algorithmic
approaches for predicting the recovery of comatose patients after cardiac arrest. A patient's …

As part of the George B. Moody PhysioNet Challenge 2023, our team (am_vision) presents a
novel approach to prognosticate the outcomes of postanoxic comatose patients based on …

Electroencephalography (EEG) features are known to predict neurological outcomes of
patients in coma after cardiac arrest, but the association between EEG features and …