Multimodal algorithms for the classification of circulation states during out-of-hospital cardiac arrest

A Elola, E Aramendi, U Irusta… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Goal: Identifying the circulation state during out-of-hospital cardiac arrest (OHCA) is
essential to determine what life-saving therapies to apply. Currently algorithms discriminate …

[HTML][HTML] A machine learning model for the prognosis of pulseless electrical activity during out-of-hospital cardiac arrest

J Urteaga, E Aramendi, A Elola, U Irusta, A Idris - Entropy, 2021 - mdpi.com
Pulseless electrical activity (PEA) is characterized by the disassociation of the mechanical
and electrical activity of the heart and appears as the initial rhythm in 20–30% of out-of …

[HTML][HTML] Fuzzy and sample entropies as predictors of patient survival using short ventricular fibrillation recordings during out of hospital cardiac arrest

B Chicote, U Irusta, E Aramendi, R Alcaraz, JJ Rieta… - Entropy, 2018 - mdpi.com
Optimal defibrillation timing guided by ventricular fibrillation (VF) waveform analysis would
contribute to improved survival of out-of-hospital cardiac arrest (OHCA) patients by …

[HTML][HTML] Prediction and Detection of Ventricular Fibrillation Using Complex Features and AI-Based Classification

M Fira, HN Costin, L Goras - Applied Sciences, 2024 - mdpi.com
We analyzed the possibility of detecting and predicting ventricular fibrillation (VF), a medical
emergency that may put people's lives at risk, as the medical prognosis depends on the time …

[HTML][HTML] Machine learning model to predict evolution of pulseless electrical activity during in-hospital cardiac arrest

J Urteaga, A Elola, A Norvik, E Unneland, TC Eftestøl… - Resuscitation …, 2024 - Elsevier
Background During pulseless electrical activity (PEA) the cardiac mechanical and electrical
functions are dissociated, a phenomenon occurring in 25–42% of in-hospital cardiac arrest …

ECG rhythm analysis during manual chest compressions using an artefact removal filter and random forest classifiers

I Isasi, AB Rad, U Irusta, M Zabihi… - 2018 Computing in …, 2018 - ieeexplore.ieee.org
Interruptions in cardiopulmonary resuscitation (CPR) decrease the chances of survival.
However, CPR must be interrupted for a reliable rhythm analysis because chest …

Predicting Ventricular Defibrillation Results Using Learning Models: A Design Practice and Performance Analysis

DC Ling, MS Tsai, DA Ling… - IEEE Open Journal of …, 2021 - ieeexplore.ieee.org
This work proposes a learning model to predict the outcome of electrical defibrillation from
ECG signals in ventricular fibrillation (VF) periods, which is a lethal situation happening …

A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest

J Urteaga Urizarbarrena, E Aramendi Ecenarro… - 2021 - lareferencia.info
Pulseless electrical activity (PEA) is characterized by the disassociation of the mechanical
and electrical activity of the heart and appears as the initial rhythm in 20–30% of out-of …

[引用][C] Multimodal Algorithms for the Classification of Circulation States during Out-of-Hospital Cardiac Arrest

A Elola Artano, E Aramendi Ecenarro… - 2020 - IEEE

[引用][C] Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest

B Chicote Gutiérrez, U Irusta Zarandona… - 2018 - MDPI