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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorAyala, Unai
dc.contributor.otherIsasi, Iraia
dc.contributor.otherIrusta, Unai
dc.contributor.otherAramendi, Elisabete
dc.contributor.otherAlonso, Erik
dc.contributor.otherKramer-Johansen, Jo
dc.contributor.otherEftestøl, Trygve
dc.date.accessioned2022-07-28T07:21:26Z
dc.date.available2022-07-28T07:21:26Z
dc.date.issued2018
dc.identifier.isbn9781728109589en
dc.identifier.issn2325-887Xen
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=153553en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/5645
dc.description.abstractMechanically delivered chest compressions induce artifacts in the ECG that can lead to an incorrect diagnosis of the shock advice algorithms implemented in the defibrillators. This forces the rescuer to stop cardiopulmonary resuscitation (CPR) compromising circulation and thus reducing the probability of survival. This paper introduces a new approach for a reliable rhythm analysis during mechanical compressions which consists of an artifact supression filter based on the recursive least squares algorithm, and a shock/no-shock decision algorithm based on machine learning techniques that uses features obtained from the filtered ECG. Data were collected from 230 out-of-hospital cardiac arrest patients treated with the LUCAS CPR device. The underlying rhythms were annotated in artifact-free intervals by consesus of expert resuscitation rhythm reviewers. Shock/no-shock diagnoses obtained through the decision algorithm were compared with the rhythm annotations to obtain the sensitivity (Se), specificity (Sp) and balanced accuracy (BAC) of the method. The results obtained were: 94.7% (Se), 97.1% (Sp) and 95.9% (BAC).en
dc.language.isoengen
dc.publisherIEEEen
dc.rights© 2018 The Authorsen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCardiopulmonary Resuscitationen
dc.subjectThoraxen
dc.subjectCompression depthen
dc.titleAn Accurate Shock Advise Algorithm for Use During Piston-Driven Chest Compressionsen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dcterms.accessRightsinfo:eu-repo/semantics/openAccessen
dcterms.sourceComputing in Cardiology Conference (CinC). Maastricht, Netherlandsen
dc.description.versioninfo:eu-repo/semantics/publishedVersionen
local.contributor.groupTeoría de la señal y comunicacioneses
local.description.peerreviewedtrueen
local.description.publicationfirstpage23en
local.description.publicationlastpage26en
local.identifier.doihttp://dx.doi.org/10.22489/CinC.2018.204en
local.contributor.otherinstitutionEuskal Herriko Unibertsitatea (EHU)eu
local.contributor.otherinstitutionOslo University Hospitalen
local.contributor.otherinstitutionUniversity of Stavangeren
local.source.detailsVol.45, 23-26 September. IEEE, 2018en


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Except where otherwise noted, this item's license is described as Attribution 4.0 International