Literature DB >> 18232347

Automatic identification of return of spontaneous circulation during cardiopulmonary resuscitation.

Martin Risdal1, Sven Ole Aase, Jo Kramer-Johansen, Trygve Eftestøl.   

Abstract

The main problem during pulse check in out-of-hospital cardiac arrest is the discrimination between normal pulse-generating rhythm (PR) and pulseless electrical activity (PEA). It has been suggested that circulatory information can be acquired by measuring the thoracic impedance via the defibrillator pads. To investigate this, we performed an experimental study where we retrospectively analyzed 127 PEA segments and 91 PR segments out of 219 and 113 segments. A PEA versus PR classification framework was developed, that uses short segments (< 10 s) of ECG and impedance measurements to discriminate between the two rhythms. Using realistic data analyzed over a duration of 3 s, our system correctly identifies 90.0% of the segments with rhythm being pulseless electrical activity, and 91.5% of the normal pulse rhythm segments. Automatic identification of pulse could avoid unnecessary pulse checks and thereby reduce no-flow time and potentially increase the chance of survival.

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Year:  2008        PMID: 18232347     DOI: 10.1109/TBME.2007.910644

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  4 in total

1.  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

2.  Assessment of the evolution of end-tidal carbon dioxide within chest compression pauses to detect restoration of spontaneous circulation.

Authors:  Jose Julio Gutiérrez; Mikel Leturiondo; Sofía Ruiz de Gauna; Jesus María Ruiz; Izaskun Azcarate; Digna María González-Otero; Juan Francisco Urtusagasti; James Knox Russell; Mohamud Ramzan Daya
Journal:  PLoS One       Date:  2021-05-18       Impact factor: 3.240

3.  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

4.  Towards the automated analysis and database development of defibrillator data from cardiac arrest.

Authors:  Trygve Eftestøl; Lawrence D Sherman
Journal:  Biomed Res Int       Date:  2014-01-12       Impact factor: 3.411

  4 in total

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