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dc.contributor.authorElola Artano, Andoni
dc.contributor.authorAramendi Ecenarro, Elisabete
dc.contributor.authorIrusta Zarandona, Unai
dc.contributor.authorPer Olav, Berve
dc.contributor.authorWik, Lars
dc.date.accessioned2024-02-08T11:03:16Z
dc.date.available2024-02-08T11:03:16Z
dc.date.issued2020-10-12
dc.identifier.citationIEEE Transactions on Biomedical Engineering 68(6) : 1913-1922 (2021)
dc.identifier.issn0018-9294
dc.identifier.issn1558-2531
dc.identifier.urihttp://hdl.handle.net/10810/65401
dc.description.abstractGoal: Identifying the circulation state during out-of-hospital cardiac arrest (OHCA) is essential to determine what life-saving therapies to apply. Currently algorithms discriminate circulation (pulsed rhythms, PR) from no circulation (pulseless electrical activity, PEA), but PEA can be classified into true (TPEA) and pseudo (PPEA) depending on cardiac contractility. This study introduces multi-class algorithms to automatically determine circulation states during OHCA using the signals available in defibrillators. Methods: A cohort of 60 OHCA cases were used to extract a dataset of 2506 5-s segments, labeled as PR (1463), PPEA (364) and TPEA (679) using the invasive blood pressure, experimentally recorded through a radial/femoral cannulation. A multimodal algorithm using features obtained from the electrocardiogram, the thoracic impedance and the capnogram was designed. A random forest model was trained to discriminate three (TPEA/PPEA/PR) and two (PEA/PR) circulation states. The models were evaluated using repeated patient-wise 5-fold cross-validation, with the unweighted mean of sensitivities (UMS) and F 1 -score as performance metrics. Results: The best model for 3-class had a median (interquartile range, IQR) UMS and F 1 of 69.0% (68.0-70.1) and 61.7% (61.0-62.5), respectively. The best two class classifier had median (IQR) UMS and F 1 of 83.9% (82.9-84.5) and 76.2% (75.0-76.9), outperforming all previous proposals in over 3-points in UMS. Conclusions: The first multiclass OHCA circulation state classifier was demonstrated. The method improved previous algorithms for binary pulse/no-pulse decisions. Significance: Automatic multiclass circulation state classification during OHCA could contribute to improve cardiac arrest therapy and improve survival rates.es_ES
dc.description.sponsorshipThis work was supported by the Spanish Ministerio de Ciencia, Innovación 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 0100
dc.language.isoenges_ES
dc.publisherIEEE
dc.relationinfo:eu-repo/grantAgreement/MICIN/RTI2018-101475-BI00
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectrandom forestes_ES
dc.subjectmachine learninges_ES
dc.subjectcardiac arrestes_ES
dc.subjectpulsed rhythmes_ES
dc.subjectpulseless electrical activityes_ES
dc.subjectpseudo-pulseless electrical activityes_ES
dc.titleMultimodal Algorithms for the Classification of Circulation States during Out-of-Hospital Cardiac Arrestes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder(c) 2021 IEEE*
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9220816
dc.identifier.doi10.1109/TBME.2020.3030216
dc.departamentoesTecnología electrónicaes_ES
dc.departamentoeuTeknologia elektronikoaes_ES


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