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dc.creatorIvanović, Marija D.
dc.creatorHannink, Julius
dc.creatorRing, Matthias
dc.creatorBaronio, Fabio
dc.creatorVukčević, Vladan
dc.creatorHadžievski, Ljupčo
dc.creatorEskofier, Bjoern
dc.date.accessioned2021-10-12T08:19:58Z
dc.date.available2021-10-12T08:19:58Z
dc.date.issued2020
dc.identifier.issn0933-3657
dc.identifier.urihttps://vinar.vin.bg.ac.rs/handle/123456789/9678
dc.description.abstractOptimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previous studies employed conventional machine learning approaches and hand-crafted features to address this issue, but none have achieved superior performance to be widely accepted. This study proposes a novel approach in which predictive features are automatically learned.MethodsA raw 4s VF episode immediately prior to first defibrillation shock was feed to a 3-stage CNN feature extractor. Each stage was composed of 4 components: convolution, rectified linear unit activation, dropout and max-pooling. At the end of feature extractor, the feature map was flattened and connected to a fully connected multi-layer perceptron for classification. For model evaluation, a 10 fold cross-validation was employed. To balance classes, SMOTE oversampling method has been applied to minority class.ResultsThe obtained results show that the proposed model is highly accurate in predicting defibrillation outcome (Acc = 93.6 %). Since recommendations on classifiers suggest at least 50 % specificity and 95 % sensitivity as safe and useful predictors for defibrillation decision, the reported sensitivity of 98.8 % and specificity of 88.2 %, with the analysis speed of 3 ms/input signal, indicate that the proposed model possesses a good prospective to be implemented in automated external defibrillators.ConclusionsThe learned features demonstrate superiority over hand-crafted ones when performed on the same dataset. This approach benefits from being fully automatic by fusing feature extraction, selection and classification into a single learning model. It provides a superior strategy that can be used as a tool to guide treatment of OHCA patients in bringing optimal decision of precedence treatment. Furthermore, for encouraging replicability, the dataset has been made publicly available to the research community.en
dc.languageen
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/691051/EU//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/45010/RS//
dc.relationHeisenberg professorship programme (grant No. ES 434/8-1)
dc.relationFederal Ministry of Education and Research of Germany (grant No. 01IS17070)
dc.rightsrestrictedAccess
dc.sourceArtificial Intelligence in Medicine
dc.subjectConvolutional neural networks (CNN)en
dc.subjectDeep learningen
dc.subjectDefibrillationen
dc.subjectShock outcomeen
dc.subjectVentricular fibrillation (VF)en
dc.titlePredicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature designen
dc.typearticleen
dc.rights.licenseARR
dcterms.abstractИвановић, Марија Д.; Ринг, Маттхиас; Баронио, Фабио; Хаджиевски, Љупчо; Ханнинк, Јулиус; Вукчевић, Владан; Ескофиер, Бјоерн;
dc.citation.volume110
dc.citation.spage101963
dc.identifier.wos000595170100001
dc.identifier.doi10.1016/j.artmed.2020.101963
dc.citation.rankM21
dc.identifier.pmid33250144
dc.type.versionpublishedVersion
dc.identifier.scopus2-s2.0-85092661204


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