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Application of entropy-based features to predict defibrillation outcome in cardiac arrest
(MDPI, 2016-08)
Prediction of defibrillation success is of vital importance to guide therapy and improve the survival of patients suffering out-of-hospital cardiac arrest (OHCA). Currently, the most efficient methods to predict shock ...
Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators
(Public Library Science, 2016-07-21)
Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survival of out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrillators (AED). AED algorithms for ...