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dc.contributor.authorElola, Andoni
dc.contributor.authorAramendi, Elisabete
dc.contributor.authorIrusta, Unai
dc.contributor.authorPicón, Artzai
dc.contributor.authorAlonso, Erik
dc.contributor.authorOwens, Pamela
dc.contributor.authorIdris, Ahamed
dc.date.accessioned2019-04-15T12:04:08Z
dc.date.available2019-04-15T12:04:08Z
dc.date.issued2019-03-01
dc.identifier.citationElola, Andoni, Elisabete Aramendi, Unai Irusta, Artzai Picón, Erik Alonso, Pamela Owens, and Ahamed Idris. “Deep Neural Networks for ECG-Based Pulse Detection During Out-of-Hospital Cardiac Arrest.” Entropy 21, no. 3 (March 21, 2019): 305. doi:10.3390/e21030305.en
dc.identifier.issn1099-4300en
dc.identifier.urihttp://hdl.handle.net/11556/704
dc.description.abstractThe 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.en
dc.description.sponsorshipThis work was supported by: The Spanish Ministerio de Economía y Competitividad, TEC2015-64678-R, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), UPV/EHU via GIU17/031 and the Basque Government through the grant PRE_2018_2_0260.en
dc.language.isoengen
dc.publisherMDPI AGen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleDeep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arresten
dc.typearticleen
dc.identifier.doi10.3390/e21030305en
dc.rights.accessRightsopenAccessen
dc.subject.keywordsPulse detectionen
dc.subject.keywordsECGen
dc.subject.keywordsPulseless electrical activityen
dc.subject.keywordsOut-of-hospital cardiac arresten
dc.subject.keywordsConvolutional neural networken
dc.subject.keywordsDeep learningen
dc.subject.keywordsBayesian optimizationen
dc.issue.number3en
dc.journal.titleEntropyen
dc.page.initial305en
dc.volume.number21en


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