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Circulation assessment by automated external defibrillators during cardiopulmonary resuscitation.
Ruiz, Jesus M; Ruiz de Gauna, Sofía; González-Otero, Digna M; Saiz, Purificación; Gutiérrez, J Julio; Veintemillas, Jose F; Bastida, Jose M; Alonso, Daniel.
Afiliação
  • Ruiz JM; Department of Communications Engineering, University of the Basque Country, UPV/EHU, 48013 Bilbao, Spain.
  • Ruiz de Gauna S; Department of Communications Engineering, University of the Basque Country, UPV/EHU, 48013 Bilbao, Spain. Electronic address: sofia.ruizdegauna@ehu.eus.
  • González-Otero DM; Department of Communications Engineering, University of the Basque Country, UPV/EHU, 48013 Bilbao, Spain.
  • Saiz P; Department of Communications Engineering, University of the Basque Country, UPV/EHU, 48013 Bilbao, Spain.
  • Gutiérrez JJ; Department of Communications Engineering, University of the Basque Country, UPV/EHU, 48013 Bilbao, Spain.
  • Veintemillas JF; Emergentziak-Osakidetza, Basque Country Health System, Basque Country, Spain.
  • Bastida JM; Emergentziak-Osakidetza, Basque Country Health System, Basque Country, Spain.
  • Alonso D; Emergentziak-Osakidetza, Basque Country Health System, Basque Country, Spain.
Resuscitation ; 128: 158-163, 2018 07.
Article em En | MEDLINE | ID: mdl-29733921
ABSTRACT

AIM:

To design and evaluate a simple algorithm able to discriminate pulsatile rhythms from pulseless electrical activity during automated external defibrillator (AED) analysis intervals, using the ECG and the transthoracic impedance (TI) acquired from defibrillation pads.

METHODS:

ECG and TI signals from out-of-hospital AED recordings were retrospectively analysed. Experts annotated the cardiac rhythm during AED analysis intervals and at the end of each episode. We developed an algorithm to classify 3-s segments of non-shockable and non-asystole rhythms as either pulsatile rhythm or pulseless electrical activity. The algorithm consisted on a decision tree based on two features the mean power of the TI segment and the mean cross-power between ECG and TI segments.

RESULTS:

From the 302 annotated episodes, 167 contained segments eligible for the study. The circulation detector algorithm presented a sensitivity (ability of detecting pulsatile rhythms) of 98.3% (95% CI 95.1-100) and a specificity (ability to detect pulseless electrical activity) of 98.4% (95% CI 97.1-99.8) in the validation subset. Absence of pulsatile rhythm was confirmed during the first AED analysis interval in 98.9% of the episodes, and presence of a pulse was confirmed in the first 3 s of all intervals with annotated return of spontaneous circulation.

CONCLUSION:

Accurate automated detection of circulation based on TI and ECG is possible during AED analysis intervals. This functionality could potentially contribute to enhance patient's care by laypersons using AEDs.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fluxo Pulsátil / Cardiografia de Impedância / Desfibriladores / Eletrocardiografia / Parada Cardíaca Extra-Hospitalar Tipo de estudo: Observational_studies / Prognostic_studies Limite: Female / Humans / Male Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fluxo Pulsátil / Cardiografia de Impedância / Desfibriladores / Eletrocardiografia / Parada Cardíaca Extra-Hospitalar Tipo de estudo: Observational_studies / Prognostic_studies Limite: Female / Humans / Male Idioma: En Ano de publicação: 2018 Tipo de documento: Article