Literature DB >> 22322285

A high-temporal resolution algorithm to discriminate shockable from nonshockable rhythms in adults and children.

Unai Irusta1, Jesús Ruiz, Elisabete Aramendi, Sofía Ruiz de Gauna, Unai Ayala, Erik Alonso.   

Abstract

AIM: To design the core algorithm of a high-temporal resolution rhythm analysis algorithm for automated external defibrillators (AEDs) valid for adults and children. Records from adult and paediatric patients were used all together to optimize and test the performance of the algorithm.
METHODS: A total of 574 shockable and 1126 nonshockable records from 1379 adult patients, and 57 shockable and 503 nonshockable records from 377 children aged between 1 and 8 years were used. The records were split into two groups for development and testing. The core algorithm analyses ECG segments of 3.2s duration and classifies the segments as nonshockable or likely shockable combining a time, slope and frequency domain analysis to detect normally conducted QRS complexes.
RESULTS: The algorithm correctly identified 98% of nonshockable segments, 97.5% in adults and 98.4% in children, and identified 99.5% of shockable segments as likely shockable, 100% in adults and 96% in children. When likely shockable segments were further analysed in terms of regularity, spectral content and heart rate to form a complete rhythm analysis algorithm the overall specificity increased to 99.6% and the sensitivity was 99.1%.
CONCLUSION: Paediatric and adult rhythms can be accurately diagnosed using 3.2s ECG segments. A single algorithm safe for children and adults can simplify AED use, and its high temporal resolution shortens pre-shock pauses which may contribute to improve resuscitation outcome.
Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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Year:  2012        PMID: 22322285     DOI: 10.1016/j.resuscitation.2012.01.032

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


  7 in total

1.  [Effects of automated external defibrillators on hands-off intervals in lay rescuers].

Authors:  Volker Schäfer; Patrick Witwer; Lisa Schwingshackl; Hannah Salchner; Lukas Gasteiger; Wilfried Schabauer; Wolfgang Lederer
Journal:  Notf Rett Med       Date:  2022-07-05       Impact factor: 0.892

2.  Circulation detection using the electrocardiogram and the thoracic impedance acquired by defibrillation pads.

Authors:  Erik Alonso; Elisabete Aramendi; Mohamud Daya; Unai Irusta; Beatriz Chicote; James K Russell; Larisa G Tereshchenko
Journal:  Resuscitation       Date:  2015-12-17       Impact factor: 5.262

3.  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

4.  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

5.  Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest.

Authors:  Andoni Elola; Elisabete Aramendi; Unai Irusta; Artzai Picón; Erik Alonso; Pamela Owens; Ahamed Idris
Journal:  Entropy (Basel)       Date:  2019-03-21       Impact factor: 2.524

Review 6.  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

7.  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 in total

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