Literature DB >> 25619441

Fully automatic rhythm analysis during chest compression pauses.

U Ayala1, U Irusta2, J Ruiz2, S Ruiz de Gauna2, D González-Otero2, E Alonso2, J Kramer-Johansen3, H Naas3, T Eftestøl4.   

Abstract

AIM: Chest compression artefacts impede a reliable rhythm analysis during cardiopulmonary resuscitation (CPR). These artefacts are not present during ventilations in 30:2 CPR. The aim of this study is to prove that a fully automatic method for rhythm analysis during ventilation pauses in 30:2 CPR is reliable an accurate.
METHODS: For this study 1414min of 30:2 CPR from 135 out-of-hospital cardiac arrest cases were analysed. The data contained 1942 pauses in compressions longer than 3.5s. An automatic pause detector identified the pauses using the transthoracic impedance, and a shock advice algorithm (SAA) diagnosed the rhythm during the detected pauses. The SAA analysed 3-s of the ECG during each pause for an accurate shock/no-shock decision.
RESULTS: The sensitivity and PPV of the pause detector were 93.5% and 97.3%, respectively. The sensitivity and specificity of the SAA in the detected pauses were 93.8% (90% low CI, 90.0%) and 95.9% (90% low CI, 94.7%), respectively. Using the method, shocks would have been advanced in 97% of occasions. For patients in nonshockable rhythms, rhythm reassessment pauses would be avoided in 95.2% (95% CI, 91.6-98.8) of occasions, thus increasing the overall chest compression fraction (CCF).
CONCLUSION: An automatic method could be used to safely analyse the rhythm during ventilation pauses. This would contribute to an early detection of refibrillation, and to increase CCF in patients with nonshockable rhythms.
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Automated external defibrillator (AED); Cardiac arrest; Cardiopulmonary resuscitation (CPR); Chest compression; Transthoracic impedance

Mesh:

Year:  2015        PMID: 25619441     DOI: 10.1016/j.resuscitation.2014.11.022

Source DB:  PubMed          Journal:  Resuscitation        ISSN: 0300-9572            Impact factor:   5.262


  5 in total

1.  Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms.

Authors:  Vessela Krasteva; Sarah Ménétré; Jean-Philippe Didon; Irena Jekova
Journal:  Sensors (Basel)       Date:  2020-05-19       Impact factor: 3.576

2.  Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia.

Authors:  Artzai Picon; Unai Irusta; Aitor Álvarez-Gila; Elisabete Aramendi; Felipe Alonso-Atienza; Carlos Figuera; Unai Ayala; Estibaliz Garrote; Lars Wik; Jo Kramer-Johansen; Trygve Eftestøl
Journal:  PLoS One       Date:  2019-05-20       Impact factor: 3.240

3.  Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest.

Authors:  Beatriz Chicote; Unai Irusta; Elisabete Aramendi; Raúl Alcaraz; José Joaquín Rieta; Iraia Isasi; Daniel Alonso; María Del Mar Baqueriza; Karlos Ibarguren
Journal:  Entropy (Basel)       Date:  2018-08-09       Impact factor: 2.524

Review 4.  A review of progress and an advanced method for shock advice algorithms in automated external defibrillators.

Authors:  Minh Tuan Nguyen; Thu-Hang T Nguyen; Hai-Chau Le
Journal:  Biomed Eng Online       Date:  2022-04-02       Impact factor: 2.819

5.  Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators.

Authors:  Carlos Figuera; Unai Irusta; Eduardo Morgado; Elisabete Aramendi; Unai Ayala; Lars Wik; Jo Kramer-Johansen; Trygve Eftestøl; Felipe Alonso-Atienza
Journal:  PLoS One       Date:  2016-07-21       Impact factor: 3.240

  5 in total

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