Rhythm Analysis during Cardiopulmonary Resuscitation Using Convolutional Neural Networks
dc.contributor.author | Isasi Liñero, Iraia | |
dc.contributor.author | Irusta Zarandona, Unai | |
dc.contributor.author | Aramendi Ecenarro, Elisabete | |
dc.contributor.author | Eftestøl, Trygve | |
dc.contributor.author | Kramer Johansen, Jo | |
dc.contributor.author | Wik, Lars | |
dc.date.accessioned | 2020-07-02T10:28:21Z | |
dc.date.available | 2020-07-02T10:28:21Z | |
dc.date.issued | 2020-05-27 | |
dc.identifier.citation | Entropy 22(6) : (2020) // article ID 595 | es_ES |
dc.identifier.issn | 1099-4300 | |
dc.identifier.uri | http://hdl.handle.net/10810/44823 | |
dc.description.abstract | Chest 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.sponsorship | This 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.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | |
dc.subject | out-of-hospital cardiac arrest (OHCA) | es_ES |
dc.subject | cardiopulmonary resuscitation (CPR) | es_ES |
dc.subject | electrocardiogram (ECG) | es_ES |
dc.subject | adaptive filter | es_ES |
dc.subject | deep learning | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | convolutional neural network (CNN) | es_ES |
dc.subject | random forest (RF) classifier | es_ES |
dc.title | Rhythm Analysis during Cardiopulmonary Resuscitation Using Convolutional Neural Networks | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2020-06-30T16:26:56Z | |
dc.rights.holder | 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/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2073-4344/10/6/657 | es_ES |
dc.identifier.doi | 10.3390/e22060595 | |
dc.departamentoes | Ingeniería de comunicaciones | |
dc.departamentoeu | Komunikazioen ingeniaritza |
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Except where otherwise noted, this item's license is described as 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/).