Abstract

The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.

Highlights

  • Out-of-hospital cardiac arrest (OHCA) remains a major public health problem, with350,000–700,000 individuals per year affected in Europe and survival rates below 10% [1,2].Early recognition of OHCA is key for survival [3] as it allows a rapid activation of the emergency system and facilitates bystander cardiopulmonary resuscitation (CPR)

  • Pulse detection during OHCA is still an unsolved problem, and there is a need for automatic methods to assist the rescuer to decide whether the patient has pulse or not [10]

  • To the best of our knowledge, this is the first study that uses deep neural network (DNN) models to discriminate between pulse-generating rhythm (PR) and pulseless electrical activity (PEA) rhythms using exclusively the ECG

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Summary

Introduction

Out-of-hospital cardiac arrest (OHCA) remains a major public health problem, with. Recognition of OHCA is key for survival [3] as it allows a rapid activation of the emergency system and facilitates bystander cardiopulmonary resuscitation (CPR). The main goal of OHCA treatment is to achieve return of spontaneous circulation (ROSC), so that post-resuscitation care can be initiated and the patient can be transported to hospital. Recognition and post-resuscitation care are two key factors. Entropy 2019, 21, 305 for the survival of the patient, and both these factors require the accurate detection of presence/absence of pulse. Healthcare professionals check for pulse by manual palpation of the carotid artery or by looking for signs of life. Carotid pulse palpation has been proven inaccurate

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