Literature DB >> 23402965

Feasibility of automated rhythm assessment in chest compression pauses during cardiopulmonary resuscitation.

J Ruiz1, U Ayala, S Ruiz de Gauna, U Irusta, D González-Otero, E Alonso, J Kramer-Johansen, T Eftestøl.   

Abstract

AIM: To demonstrate the feasibility of doing a reliable rhythm analysis in the chest compression pauses (e.g. pauses for two ventilations) during cardiopulmonary resuscitation (CPR).
METHODS: We extracted 110 shockable and 466 nonshockable segments from 235 out-of-hospital cardiac arrest episodes. Pauses in chest compressions were already annotated in the episodes. We classified pauses as ventilation or non-ventilation pause using the transthoracic impedance. A high-temporal resolution shock advice algorithm (SAA) that gives a shock/no-shock decision in 3s was launched once for every pause longer than 3s. The sensitivity and specificity of the SAA for the analyses during the pauses were computed.
RESULTS: We identified 4476 pauses, 3263 were ventilation pauses and 2183 had two ventilations. The median of the mean duration per segment of all pauses and of pauses with two ventilations were 6.1s (4.9-7.5s) and 5.1s (4.2-6.4s), respectively. A total of 91.8% of the pauses and 95.3% of the pauses with two ventilations were long enough to launch the SAA. The overall sensitivity and specificity were 95.8% (90% low one-sided CI, 94.3%) and 96.8% (CI, 96.2%), respectively. There were no significant differences between the sensitivities (P=0.84) and the specificities (P=0.18) for the ventilation and the non-ventilation pauses.
CONCLUSION: Chest compression pauses are frequent and of sufficient duration to launch a high-temporal resolution SAA. During these pauses rhythm analysis was reliable. Pre-shock pauses could be minimised by analysing the rhythm during ventilation pauses when CPR is delivered at 30:2 compression:ventilation ratio.
Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Automated external defibrillator (AED); Cardiopulmonary resuscitation (CPR); Defibrillation; Hands-off; Out-of-hospital cardiac arrest (OHCA); Ventilation

Mesh:

Year:  2013        PMID: 23402965     DOI: 10.1016/j.resuscitation.2013.01.034

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


  6 in total

1.  Automatic identification of compressions and ventilations during CPR based on the fuzzy c-means clustering and deep belief network.

Authors:  He-Hua Zhang; Li Yang; An-Hai Wei; Ao-Wen Duan; Yong-Ming Li; Ping Zhao; Yong-Qin Li
Journal:  Ann Transl Med       Date:  2020-09

2.  Detection of spontaneous pulse using the acceleration signals acquired from CPR feedback sensor in a porcine model of cardiac arrest.

Authors:  Liang Wei; Gang Chen; Zhengfei Yang; Tao Yu; Weilun Quan; Yongqin Li
Journal:  PLoS One       Date:  2017-12-08       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.  Rhythm analysis during cardiopulmonary resuscitation: past, present, and future.

Authors:  Sofia Ruiz de Gauna; Unai Irusta; Jesus Ruiz; Unai Ayala; Elisabete Aramendi; Trygve Eftestøl
Journal:  Biomed Res Int       Date:  2014-01-09       Impact factor: 3.411

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

6.  Monitoring chest compression rate in automated external defibrillators using the autocorrelation of the transthoracic impedance.

Authors:  Sofía Ruiz de Gauna; Jesus María Ruiz; Jose Julio Gutiérrez; Digna María González-Otero; Daniel Alonso; Carlos Corcuera; Juan Francisco Urtusagasti
Journal:  PLoS One       Date:  2020-09-30       Impact factor: 3.240

  6 in total

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