Literature DB >> 20069371

Shock advisory system for heart rhythm analysis during cardiopulmonary resuscitation using a single ECG input of automated external defibrillators.

Vessela Krasteva1, Irena Jekova, Ivan Dotsinsky, Jean-Philippe Didon.   

Abstract

Minimum "hands-off" intervals during cardiopulmonary resuscitation (CPR) are required to improve the success rate of defibrillation. In support of such life-saving practice, a shock advisory system (SAS) for automatic analysis of the electrocardiogram (ECG) contaminated by chest compression (CC) artefacts is presented. Ease of use for the automated external defibrillators (AEDs) is aimed and therefore only processing of ECG from usual defibrillation pads is required. The proposed SAS relies on assessment of outstanding components of ECG rhythms and CC artefacts in the time and frequency domain. For this purpose, three criteria are introduced to derive quantitative measures of band-pass filtered CC-contaminated ECGs, combined with three more criteria for frequency-band evaluation of reconstructed ECGs (rECG). The rECGs are derived by specific techniques for CC waves similarity assessment and are reproducing to some extent the underlying ECG rhythms. The rhythm classifier embedded in SAS takes a probabilistic decision designed by statistics on the training dataset. Both training and testing are fully performed on real CC-contaminated strips of 10 s extracted from human ECGs of out-of-hospital cardiac arrest interventions. The testing is done on 172 shockable strips (ventricular fibrillations VF), 371 non-shockable strips (NR) and 330 asystoles (ASYS). The achieved sensitivity of 90.1% meets the AHA performance goal for noise-free VF (>90%). The specificity of 88.5% for NR and 83.3% for ASYS are comparable or even better than accuracy reported in literature. It is important to note that, the aim of this SAS is not to recommend shock delivery but to advice the rescuers to "Continue CPR" or to "Stop CPR and Prepare for Shock" thus minimizing "hands-off" intervals.

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Year:  2010        PMID: 20069371     DOI: 10.1007/s10439-009-9885-9

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  7 in total

1.  Combining Amplitude Spectrum Area with Previous Shock Information Using Neural Networks Improves Prediction Performance of Defibrillation Outcome for Subsequent Shocks in Out-Of-Hospital Cardiac Arrest Patients.

Authors:  Mi He; Yubao Lu; Lei Zhang; Hehua Zhang; Yushun Gong; Yongqin Li
Journal:  PLoS One       Date:  2016-02-10       Impact factor: 3.240

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.  Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation.

Authors:  Shirin Hajeb-M; Alicia Cascella; Matt Valentine; K H Chon
Journal:  J Am Heart Assoc       Date:  2021-03-05       Impact factor: 5.501

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.  A reliable method for rhythm analysis during cardiopulmonary resuscitation.

Authors:  U Ayala; U Irusta; J Ruiz; T Eftestøl; J Kramer-Johansen; F Alonso-Atienza; E Alonso; D González-Otero
Journal:  Biomed Res Int       Date:  2014-05-07       Impact factor: 3.411

6.  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

7.  Computational Model for Therapy Optimization of Wearable Cardioverter Defibrillator: Shockable Rhythm Detection and Optimal Electrotherapy.

Authors:  Oishee Mazumder; Rohan Banerjee; Dibyendu Roy; Ayan Mukherjee; Avik Ghose; Sundeep Khandelwal; Aniruddha Sinha
Journal:  Front Physiol       Date:  2021-12-10       Impact factor: 4.566

  7 in total

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