Shock advice algorithm plays a vital role in the detection of sudden cardiac arrests on
electrocardiogram signals and hence, brings about survival improvement by delivering …

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-
of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning …

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 …

Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be
learned to self-extract significant features of the electrocardiogram (ECG) and can generally …

Aims During out-of-hospital cardiac arrest (OHCA), an automatic external defibrillator (AED)
analyzes the cardiac rhythm every two minutes; however, 80% of refibrillations occur within …

This study aims to present a novel deep learning algorithm for a sliding shock advisory
decision during cardiopulmonary resuscitation (CPR) and its performance evaluation as a …

Goal: Accurate shock decision methods during piston-driven cardiopulmonary resuscitation
(CPR) would contribute to improve therapy and increase cardiac arrest survival rates. The …

Background Pauses in chest compressions (CCs) have a negative association with survival
from cardiac arrest. Electrocardiographic (ECG) rhythm analysis and defibrillator charging …

Cardiopulmonary resuscitation (CPR) must be interrupted for reliable rhythm analysis in
current automatic external defibrillators because of artifacts produced by chest …

Automated External Defibrillators (AED) incorporate a shock decision algorithm that
analyzes the patient's electrocardiogram (EKG), allowing lay persons to provide life saving …