A Convolutional Neural Network Approach for Interpreting Cardiac Rhythms from Resuscitation of Cardiac Arrest Patients.

Patients undergoing cardiopulmonary ressuscitation (CPR) may respond through rhythm transitions between different rhythms ventricular fibrillation (VF), ventricular tachycardia (VT), asystole (AS), pulseless electrical activity (PEA) and pulse generating rhythm (PR). Rhythm recognition is crucial to address adequate resuscitation efforts, and in this study we applied a deep neural network to classify ECG rhythms during cardiac arrest. Artifact-free four second segments were extracted from 100 patients in out-of-hospital cardiac arrest. A convolutional neural network (CNN) was trained to discriminate between five cardiac arrest rhytm types. Experiments were conducted with increasing number of layers. For each model, training was repeated 10 times to explore variations in the results. A five layer network provided the best performance with an accuracy of 80. 3 (78.1,81.3)% (median(25th, 75th quartiles)). We have proposed a deep learning approach to automatically recognise five cardiac arrest rhythms common during resuscitation.