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dc.rights.licenseAttribution-NonCommercial-NoDerivatives 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.accessioned2022-07-29T09:29:20Z
dc.date.available2022-07-29T09:29:20Z
dc.date.issued2017
dc.identifier.issn2325-8861en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=150507en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/5649
dc.description.abstractPiston-driven mechanical chest compression (CC) devices induce a quasi-periodic artefact in the ECG, making rhythm diagnosis unreliable. Data from 230 out-of-hospital cardiac arrest (OHCA) patients were collected in which CCs were delivered using the piston driven LUCAS-2 device. Underlying rhythms were annotated by expert reviewers in artefact-free intervals. Two artefact removal methods (filters) were introduced: a static solution based on Goertzel’s algorithm, and an adaptive solution based on a Recursive Least Squares (RLS) filter. The filtered ECG was diagnosed by a shock/no-shock decision algorithm used in a commercial defibrillator and compared with the rhythm annotations. Filter performance was evaluated in terms of balanced accuracy (BAC), the mean of sensitivity (shockable) and specificity (nonshockable). Compared to the unfiltered signal, the static filter increased BAC by 20 points, and the RLS filter by 25 points. Adaptive filtering results in 99.0% sensitivity and 87.3% specificity.en
dc.language.isoengen
dc.publisherCinC Computing In Cardiologyen
dc.rights© 2017 The Authorsen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleRemoving piston-driven mechanical chest compression artefacts from the ECGen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dcterms.accessRightsinfo:eu-repo/semantics/openAccessen
dcterms.source44th Computing in Cardiology Conference, CinC 2017. Rennes, France. 24-27 September. Computing in Cardiologyen
dc.description.versioninfo:eu-repo/semantics/publishedVersionen
local.contributor.groupTeoría de la señal y comunicacioneses
local.description.peerreviewedtrueen
local.description.publicationfirstpage1en
local.description.publicationlastpage4en
local.identifier.doihttp://dx.doi.org/10.22489/CinC.2017.009-115en
local.contributor.otherinstitutionEuskal Herriko Unibertsitatea (EHU)eu
local.contributor.otherinstitutionNorwegian National Advisory Unit on Prehospital Emergency Medicineen
local.contributor.otherinstitutionOslo University Hospitalen
local.contributor.otherinstitutionUniversity of Osloen
local.contributor.otherinstitutionUniversity of Stavangeren
local.source.detailsVol. 44. Pp. 1-4. IEEE Computer Society, 2017en


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Attribution-NonCommercial-NoDerivatives 4.0 International
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