Deep Learning for Pulse Detection in Out-of-Hospital Cardiac Arrest Using the ECG

Andoni Elola, Elisabete Aramendi, Unai Irusta, Artzai Picon, Erik Alonso, Pamela Owens, Ahamed H Idris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Pulse detection during out-of-hospital cardiac arrest is necessary to identify cardiac arrest and detect return of spontaneous circulation. Currently, carotid pulse checking and checking for signs of life are the most widespread procedures for pulse detection, but both have been proven inaccurate and time consuming. Automatic methods that could be integrated in Automated External Defibrillators (AEDs) are needed. In this study we propose a deep neural network classifier to detect pulse using exclusively the ECG. We extracted 3914 segments of 4s from 279 patients, all of them with an organized rhythm. They were annotated as pulsed rhythm or pulseless rhythm based on clinical information. A total of 2372 pulsed rhythms and 1542 pulseless rhythms were included in the study. To train and test the model 3038 (223 patients) and 876 segments (56 patients) were used, respectively. The model was evaluated in terms of Sensitivity (Se) and Specificity (Sp) for pulse detection. The network showed a Se/Sp of 89.4%/97.2% during training process and 91.7%/92.5% for the test set.

Original languageEnglish (US)
Title of host publicationComputing in Cardiology Conference, CinC 2018
PublisherIEEE Computer Society
ISBN (Electronic)9781728109589
DOIs
StatePublished - Sep 1 2018
Event45th Computing in Cardiology Conference, CinC 2018 - Maastricht, Netherlands
Duration: Sep 23 2018Sep 26 2018

Publication series

NameComputing in Cardiology
Volume2018-September
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X

Conference

Conference45th Computing in Cardiology Conference, CinC 2018
CountryNetherlands
CityMaastricht
Period9/23/189/26/18

Fingerprint

Out-of-Hospital Cardiac Arrest
Electrocardiography
Pulse
Learning
Defibrillators
Classifiers
Sensitivity and Specificity
Heart Arrest
Deep learning
Deep neural networks

ASJC Scopus subject areas

  • Computer Science(all)
  • Cardiology and Cardiovascular Medicine

Cite this

Elola, A., Aramendi, E., Irusta, U., Picon, A., Alonso, E., Owens, P., & Idris, A. H. (2018). Deep Learning for Pulse Detection in Out-of-Hospital Cardiac Arrest Using the ECG. In Computing in Cardiology Conference, CinC 2018 [8744005] (Computing in Cardiology; Vol. 2018-September). IEEE Computer Society. https://doi.org/10.22489/CinC.2018.093

Deep Learning for Pulse Detection in Out-of-Hospital Cardiac Arrest Using the ECG. / Elola, Andoni; Aramendi, Elisabete; Irusta, Unai; Picon, Artzai; Alonso, Erik; Owens, Pamela; Idris, Ahamed H.

Computing in Cardiology Conference, CinC 2018. IEEE Computer Society, 2018. 8744005 (Computing in Cardiology; Vol. 2018-September).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Elola, A, Aramendi, E, Irusta, U, Picon, A, Alonso, E, Owens, P & Idris, AH 2018, Deep Learning for Pulse Detection in Out-of-Hospital Cardiac Arrest Using the ECG. in Computing in Cardiology Conference, CinC 2018., 8744005, Computing in Cardiology, vol. 2018-September, IEEE Computer Society, 45th Computing in Cardiology Conference, CinC 2018, Maastricht, Netherlands, 9/23/18. https://doi.org/10.22489/CinC.2018.093
Elola A, Aramendi E, Irusta U, Picon A, Alonso E, Owens P et al. Deep Learning for Pulse Detection in Out-of-Hospital Cardiac Arrest Using the ECG. In Computing in Cardiology Conference, CinC 2018. IEEE Computer Society. 2018. 8744005. (Computing in Cardiology). https://doi.org/10.22489/CinC.2018.093
Elola, Andoni ; Aramendi, Elisabete ; Irusta, Unai ; Picon, Artzai ; Alonso, Erik ; Owens, Pamela ; Idris, Ahamed H. / Deep Learning for Pulse Detection in Out-of-Hospital Cardiac Arrest Using the ECG. Computing in Cardiology Conference, CinC 2018. IEEE Computer Society, 2018. (Computing in Cardiology).
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