dc.contributor.author | Elola Artano, Andoni | |
dc.contributor.author | Aramendi Ecenarro, Elisabete | |
dc.contributor.author | Irusta Zarandona, Unai | |
dc.contributor.author | Per Olav, Berve | |
dc.contributor.author | Wik, Lars | |
dc.date.accessioned | 2024-02-08T11:03:16Z | |
dc.date.available | 2024-02-08T11:03:16Z | |
dc.date.issued | 2020-10-12 | |
dc.identifier.citation | IEEE Transactions on Biomedical Engineering 68(6) : 1913-1922 (2021) | |
dc.identifier.issn | 0018-9294 | |
dc.identifier.issn | 1558-2531 | |
dc.identifier.uri | http://hdl.handle.net/10810/65401 | |
dc.description.abstract | Goal: 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.sponsorship | This 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.iso | eng | es_ES |
dc.publisher | IEEE | |
dc.relation | info:eu-repo/grantAgreement/MICIN/RTI2018-101475-BI00 | |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | random forest | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | cardiac arrest | es_ES |
dc.subject | pulsed rhythm | es_ES |
dc.subject | pulseless electrical activity | es_ES |
dc.subject | pseudo-pulseless electrical activity | es_ES |
dc.title | Multimodal Algorithms for the Classification of Circulation States during Out-of-Hospital Cardiac Arrest | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | (c) 2021 IEEE | * |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9220816 | |
dc.identifier.doi | 10.1109/TBME.2020.3030216 | |
dc.departamentoes | Tecnología electrónica | es_ES |
dc.departamentoeu | Teknologia elektronikoa | es_ES |