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

Elola, AndoniAramendi, ElisabeteIrusta, UnaiPicón, ArtzaiAlonso, ErikOwens, PamelaIdris, Ahamed2019-03-01Elola , A , Aramendi , E , Irusta , U , Picón , A , Alonso , E , Owens , P & Idris , A 2019 , ' Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest ' , Entropy , vol. 21 , no. 3 , 305 , pp. 305 . https://doi.org/10.3390/e210303051099-4300researchoutputwizard: 11556/704Publisher Copyright: © 2019 by the authors.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.11107564enginfo:eu-repo/semantics/openAccessDeep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrestjournal article10.3390/e21030305Pulse detectionECGPulseless electrical activityOut-of-hospital cardiac arrestConvolutional neural networkDeep learningBayesian optimizationPulse detectionECGPulseless electrical activityOut-of-hospital cardiac arrestConvolutional neural networkDeep learningBayesian optimizationInformation SystemsMathematical PhysicsPhysics and Astronomy (miscellaneous)General Physics and AstronomyElectrical and Electronic EngineeringFunding InfoThis work was supported by: The Spanish Ministerio de Economía y Competitividad, TEC2015-64678-R,_x000D_ jointly with the Fondo Europeo de Desarrollo Regional (FEDER), UPV/EHU via GIU17/031 and the Basque_x000D_ Government through the grant PRE_2018_2_0260.This work was supported by: The Spanish Ministerio de Economía y Competitividad, TEC2015-64678-R,_x000D_ jointly with the Fondo Europeo de Desarrollo Regional (FEDER), UPV/EHU via GIU17/031 and the Basque_x000D_ Government through the grant PRE_2018_2_0260.http://www.scopus.com/inward/record.url?scp=85063594590&partnerID=8YFLogxK