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Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest

1
Department of Communications Engineering, University of the Basque Country, 48013 Bilbao, Spain
2
Computer Vision, TECNALIA Research & Innovation, 48160 Derio, Spain
3
Department of Engineering Systems and Automatics, University of the Basque Country, 48013 Bilbao, Spain
4
Department of Applied Mathematics, University of the Basque Country, 48013 Bilbao, Spain
5
Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(3), 305; https://doi.org/10.3390/e21030305
Received: 8 March 2019 / Accepted: 19 March 2019 / Published: 21 March 2019
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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. View Full-Text
Keywords: pulse detection; ECG; pulseless electrical activity; out-of-hospital cardiac arrest; convolutional neural network; deep learning; Bayesian optimization pulse detection; ECG; pulseless electrical activity; out-of-hospital cardiac arrest; convolutional neural network; deep learning; Bayesian optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Elola, A.; Aramendi, E.; Irusta, U.; Picón, A.; Alonso, E.; Owens, P.; Idris, A. Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest. Entropy 2019, 21, 305.

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