Automated External Defibrillators (AED) incorporate a shock decision algorithm that analyzes the patient's electrocardiogram (EKG), allowing lay persons to provide life saving defibrillation therapy to out-of-hospital cardiac arrest (OHCA) patients. The most accurate shock decision algorithms are based on deep learning, but these algorithms have not been trained and tested using OHCA data. In this study we propose novel deep learning architectures for shock decision algorithms based on convolutional and residual networks. EKG electronic recordings from a cohort of 852 OHCA cases (4216 AED EKG analyses) were used in the study. EKGs were annotated by a pool of six expert clinicians resulting in 3718 nonshockable and 498 shockable EKGs. Data were partitioned patient wise in a stratified way to train and test the models using 10-fold cross validation, and the procedure was repeated 100 times for statistical evaluation. Performance was assessed using sensitivity (shockable), specificity (non-shockable) and accuracy, and the analysis was conducted for EKG segments of decreasing duration. The best model had median (interdecile range) accuracies of 98.6 (98.5 -98.7)%, 98.4 (98.2 -98.6)%, 98.2 (97.9 -98.4)%, and 97.6 (97.4 -97.8)%, for 4, 3, 2 and 1 second EKG segments, respectively. The minimum 90% sensitivity and 95% specificity requirements established by the American Heart Association for shock decision algorithms were met, and the best model presented significantly greater accuracy (p<0.05 McNemar test) than previous deep learning solutions for all segment durations. Moreover, the first AHA compliant shock decision algorithm using 1-s segments was demonstrated. This should contribute to a combined optimization of defibrillation and cardiopulmonary resuscitation therapy to improve OHCA survival. INDEX TERMS automated external defibrillator (AED), electrocardiogram (EKG), convolutional neural networks (CNN), deep learning, ventricular fibrillation (VF), residual networks. I. INTRODUCTION 1 C ARDIAC arrest is the unexpected sudden cessation 2 of the cardiac function, and occurs mostly in a 3 pre-hospital setting. Out-of-hospital cardiac arrest (OHCA) 4 constitutes a major global health problem. Only in the US 5 one thousand OHCA events are estimated to occur daily, 6 with survival rates around 10% [1]. Two therapies are key for OHCA survival: defibrillation, to restore the normal 8 function of the heart; and cardiopulmonary resuscitation 9
In order to make aware of the importance that fiber optic sensors have in the modern industry, students in the master´s degree in Telecommunications Engineering at the University of the Basque Country are faced with a practical case of interest that provides a convenient training platform for learning important aspects of fiberbased optical sensing, such as probe design, the calibration curve, data interpretation and management, etc. The hands-on experiment runs around a tabletop rotor kit that includes different rotating parts and custom-designed fiber-based displacement sensors for monitoring the dynamics of the rotation. Practical aspects such as data interpretation and processing are dealt thoroughly, but without neglecting more fundamental aspects involved in the design of the optical probe.
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