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Multimodal Algorithms for the Classification of Circulation States During Out-of-Hospital Cardiac Arrest

descriptionPublicationkeyboard_double_arrow_right Article 01 Jun 2021 Spain Publisher:Institute of Electrical and Electronics Engineers (IEEE)Journal:IEEE Transactions on Biomedical Engineering, volume 68, pages 1,913-1,922 (issn: 0018-9294, eissn: 1558-2531,

Authors: Andoni Elola; Elisabete Aramendi; Unai Irusta; Per Olav Berve; Lars Wik;

doi: 10.1109/tbme.2020.3030216

pmid: 33044927

handle: 10810/65401

Multimodal Algorithms for the Classification of Circulation States During Out-of-Hospital Cardiac Arrest

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Abstract

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.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.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.The first multiclass OHCA circulation state classifier was demonstrated. The method improved previous algorithms for binary pulse/no-pulse decisions.Automatic multiclass circulation state classification during OHCA could contribute to improve cardiac arrest therapy and improve survival rates.

Country

Spain

Related Organizations

Keywords

cardiac arrest, pseudo-pulseless electrical activity, Cardiopulmonary Resuscitation, pulsed rhythm, Electrocardiography, machine learning, Heart Rate, Humans, random forest, Algorithms, Out-of-Hospital Cardiac Arrest, pulseless electrical activity, Retrospective Studies

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	17
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%