Feasibility of Neurological Prognostication After Pediatric Cardiac Arrest with a Reduced Number of EEG Electrodes

The authors aimed to determine whether reducing the number of EEG electrodes used for neurological prognostication after pediatric cardiac arrest (pCA) would affect the accuracy of visual assessments and machine learning models for predicting patient outcomes. Their study found that both visual classification and machine learning predictions of outcomes were comparable between full and reduced electrode montages, suggesting that fewer electrodes could be sufficient for effective monitoring without compromising prognostic accuracy.