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dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.contributor.authorAyala Fernández, Unai
dc.contributor.otherFiguera, Carlos
dc.contributor.otherIrusta, Unai
dc.contributor.otherMorgado, Eduardo
dc.contributor.otherAramendi, Elisabete
dc.contributor.otherWik, Lars
dc.contributor.otherKramer-Johansen, Jo
dc.contributor.otherEftestøl, Trygve
dc.contributor.otherAlonso-Atienza, Felipe
dc.date.accessioned2019-05-23T13:54:19Z
dc.date.available2019-05-23T13:54:19Z
dc.date.issued2016
dc.identifier.issn1932-6203en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=125971en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/1223
dc.description.abstractEarly recognition of ventricular fibrillation (VF) and electrical therapy are key for the survivalof out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrilla-tors (AED). AED algorithms for VF-detection are customarily assessed using Holter record-ings from public electrocardiogram (ECG) databases, which may be different from the ECGseen during OHCA events. This study evaluates VF-detection using data from both OHCApatients and public Holter recordings. ECG-segments of 4-s and 8-s duration were ana-lyzed. For each segment 30 features were computed and fed to state of the art machinelearning (ML) algorithms. ML-algorithms with built-in feature selection capabilities wereused to determine the optimal feature subsets for both databases. Patient-wise bootstraptechniques were used to evaluate algorithm performance in terms of sensitivity (Se), speci-ficity (Sp) and balanced error rate (BER). Performance was significantly better for publicdata with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times morefeatures than the data from public databases for an accurate detection (6 vs 3). No signifi-cant differences in performance were found for different segment lengths, the BER differ-ences were below 0.5-points in all cases. Our results show that VF-detection is morechallenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s.es
dc.language.isoengen
dc.publisherPLOSen
dc.rights© Figuera et al.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleMachine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillatorsen
dc.typeinfo:eu-repo/semantics/articleen
dcterms.accessRightsinfo:eu-repo/semantics/openAccessen
dcterms.sourcePLoS ONEen
dc.description.versioninfo:eu-repo/semantics/publishedVersionen
local.description.peerreviewedtrueen
local.identifier.doihttp://dx.doi.org/10.1371/journal.pone.0159654en
local.source.detailsVol. 11. Nº 7. July 21, 2016eu_ES


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Attribution-NonCommercial-NoDerivatives 4.0 International
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