Publikationen, an denen er mitarbeitet Elisabete Aramendi Ecenarro (35)

2021

  1. End-tidal carbon dioxide (ETCO2) and ventricular fibrillation amplitude spectral area (AMSA) for shock outcome prediction in out-of-hospital cardiac arrest. Are they two sides of the same coin?

    Resuscitation, Vol. 160, pp. 142-149

2020

  1. Shock Decision Algorithms for Automated External Defibrillators Based on Convolutional Networks

    IEEE Access, Vol. 8, pp. 154746-154758

2019

  1. A Machine Learning Shock Decision Algorithm for Use During Piston-Driven Chest Compressions

    IEEE Transactions on Biomedical Engineering, Vol. 66, Núm. 6, pp. 1752-1760

  2. A Multistage Algorithm for ECG Rhythm Analysis during Piston-Driven Mechanical Chest Compressions

    IEEE Transactions on Biomedical Engineering, Vol. 66, Núm. 1, pp. 263-272

  3. Algoritmo basado en redes neuronales recurrentes para la detección de ritmos desfibrilables

    Hacia una salud personalizada y universal. XXXVII Congreso Anual de la Sociedad Española de Ingeniería Biomédica: Actas del Congreso CASEIB 2019

  4. Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

    PLoS ONE, Vol. 14, Núm. 5

  5. Value of capnography to predict defibrillation success in out-of-hospital cardiac arrest

    Resuscitation, Vol. 138, pp. 74-81

2018

  1. An Accurate Shock Advise Algorithm for Use during Piston-Driven Chest Compressions

    Computing in Cardiology

  2. Fuzzy and sample entropies as predictors of patient survival using short ventricular fibrillation recordings during out of hospital cardiac arrest

    Entropy, Vol. 20, Núm. 8

2017

  1. Predicción del éxito de la desfibrilación mediante entropía difusa en términos de recuperación de ritmo cardíaco regular, pulso y supervivencia

    XXXV Congreso anual de la Sociedad Española de Ingeniería Biomédica: Libro de actas

  2. Removing piston-driven mechanical chest compression artefacts from the ECG

    Computing in Cardiology

2016

  1. Análisis de la influencia de la amplitud de la fibrilación ventricular en los predictores del éxito de la desfibrilación

    CASEIB 2016 XXXIV Congreso Anual de la Sociedad Española de Ingeniería Biomédica: Libro de actas

  2. Application of entropy-based features to predict defibrillation outcome in cardiac arrest

    Entropy, Vol. 18, Núm. 9

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

    Resuscitation, Vol. 99, pp. 56-62

  4. Filtering mechanical chest compression artefacts from out-of-hospital cardiac arrest data

    Resuscitation, Vol. 98, pp. 41-47

  5. Machine learning techniques for the detection of shockable rhythms in automated external defibrillators

    PLoS ONE, Vol. 11, Núm. 7

  6. Nonlinear energy operators for defibrillation shock outcome prediction

    Computing in Cardiology

2015

  1. A method to measure ventilation rate during cardiopulmonary resuscitation using the capnogram

    Computing in Cardiology

  2. Reliability and accuracy of the thoracic impedance signal for measuring cardiopulmonary resuscitation quality metrics

    Resuscitation, Vol. 88, pp. 28-34

  3. Sample entropy as a shock outcome predictor during basis life support

    Computing in Cardiology