Survival from out‐of‐hospital cardiac arrest depends largely on two factors: early
cardiopulmonary resuscitation (CPR) and early defibrillation. CPR must be interrupted for a …

High performance of the shock advisory analysis of the electrocardiogram (ECG) during
cardiopulmonary resuscitation (CPR) in out-of-hospital cardiac arrest (OHCA) is important …

Objective: There is a need to monitor the heart rhythm in resuscitation to improve treatment
quality. Resuscitation rhythms are categorized into: ventricular tachycardia (VT), ventricular …

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 …

Background We previously developed a US-CAB protocol for evaluation of circulatory-
airway-breathing status during cardiopulmonary resuscitation (CPR). This study aimed at …

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 …

Aim To develop and evaluate a method to detect circulation in the presence of organized
rhythms (ORs) during resuscitation using signals acquired by defibrillation pads. Methods …

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 …