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dc.contributor.authorIsasi Liñero, Iraia
dc.contributor.authorIrusta Zarandona, Unai
dc.contributor.authorAramendi Ecenarro, Elisabete
dc.contributor.authorEftestøl, Trygve
dc.contributor.authorKramer Johansen, Jo
dc.contributor.authorWik, Lars
dc.date.accessioned2020-07-02T10:28:21Z
dc.date.available2020-07-02T10:28:21Z
dc.date.issued2020-05-27
dc.identifier.citationEntropy 22(6) : (2020) // article ID 595es_ES
dc.identifier.issn1099-4300
dc.identifier.urihttp://hdl.handle.net/10810/44823
dc.description.abstractChest compressions during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may provoque inaccurate rhythm classification by the algorithm of the defibrillator. The objective of this study was to design an algorithm to produce reliable shock/no-shock decisions during CPR using convolutional neural networks (CNN). A total of 3319 ECG segments of 9 s extracted during chest compressions were used, whereof 586 were shockable and 2733 nonshockable. Chest compression artifacts were removed using a Recursive Least Squares (RLS) filter, and the filtered ECG was fed to a CNN classifier with three convolutional blocks and two fully connected layers for the shock/no-shock classification. A 5-fold cross validation architecture was adopted to train/test the algorithm, and the proccess was repeated 100 times to statistically characterize the performance. The proposed architecture was compared to the most accurate algorithms that include handcrafted ECG features and a random forest classifier (baseline model). The median (90% confidence interval) sensitivity, specificity, accuracy and balanced accuracy of the method were 95.8% (94.6–96.8), 96.1% (95.8–96.5), 96.1% (95.7–96.4) and 96.0% (95.5–96.5), respectively. The proposed algorithm outperformed the baseline model by 0.6-points in accuracy. This new approach shows the potential of deep learning methods to provide reliable diagnosis of the cardiac rhythm without interrupting chest compression therapy.es_ES
dc.description.sponsorshipThis work was supported by the Spanish Ministerio de Ciencia, Innovacion y Universidades through grant RTI2018-101475-BI00, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), and by the Basque Government through grants IT1229-19 and PRE-2019-2-0066.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectout-of-hospital cardiac arrest (OHCA)es_ES
dc.subjectcardiopulmonary resuscitation (CPR)es_ES
dc.subjectelectrocardiogram (ECG)es_ES
dc.subjectadaptive filteres_ES
dc.subjectdeep learninges_ES
dc.subjectmachine learninges_ES
dc.subjectconvolutional neural network (CNN)es_ES
dc.subjectrandom forest (RF) classifieres_ES
dc.titleRhythm Analysis during Cardiopulmonary Resuscitation Using Convolutional Neural Networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.date.updated2020-06-30T16:26:56Z
dc.rights.holder2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).es_ES
dc.relation.publisherversionhttps://www.mdpi.com/2073-4344/10/6/657es_ES
dc.identifier.doi10.3390/e22060595
dc.departamentoesIngeniería de comunicaciones
dc.departamentoeuKomunikazioen ingeniaritza


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2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Bestelakorik adierazi ezean, itemaren baimena horrela deskribatzen da:2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).