AIMC Topic: Cardiopulmonary Resuscitation
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Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation.
Journal of the American Heart Association
Mar 5, 2021
Background Because chest compressions induce artifacts in the ECG, current automated external defibrillators instruct the user to stop cardiopulmonary resuscitation (CPR) while an automated rhythm analysis is performed. It has been shown that minimiz...
Prediction of cerebral perfusion pressure during CPR using electroencephalogram in a swine model of ventricular fibrillation.
The American journal of emergency medicine
Feb 25, 2021
BACKGROUND: Measuring the quality of cardiopulmonary resuscitation (CPR) is important for improving outcomes in cardiac arrest. Cerebral perfusion pressure (CePP) could represent cerebral circulation during CPR, but it is difficult to measure non-inv...
Outcome prediction with resting-state functional connectivity after cardiac arrest.
Scientific reports
Jul 16, 2020
Predicting outcome in comatose patients after successful cardiopulmonary resuscitation is challenging. Our primary aim was to assess the potential contribution of resting-state-functional magnetic resonance imaging (RS-fMRI) in predicting neurologica...
Identifying the relative importance of predictors of survival in out of hospital cardiac arrest: a machine learning study.
Scandinavian journal of trauma, resuscitation and emergency medicine
Jun 25, 2020
INTRODUCTION: Studies examining the factors linked to survival after out of hospital cardiac arrest (OHCA) have either aimed to describe the characteristics and outcomes of OHCA in different parts of the world, or focused on certain factors and wheth...
Estimation of Arterial Blood Pressure Based on Artificial Intelligence Using Single Earlobe Photoplethysmography during Cardiopulmonary Resuscitation.
Journal of medical systems
Dec 10, 2019
This study investigates the feasibility of estimation of blood pressure (BP) using a single earlobe photoplethysmography (Ear PPG) during cardiopulmonary resuscitation (CPR). We have designed a system that carries out Ear PPG for estimation of BP. In...
Prediction of good neurological recovery after out-of-hospital cardiac arrest: A machine learning analysis.
Resuscitation
Jul 27, 2019
BACKGROUND: This study aimed to train, validate and compare predictive models that use machine learning analysis for good neurological recovery in OHCA patients.
Detecting Mistakes in CPR Training with Multimodal Data and Neural Networks.
Sensors (Basel, Switzerland)
Jul 13, 2019
This study investigated to what extent multimodal data can be used to detect mistakes during Cardiopulmonary Resuscitation (CPR) training. We complemented the Laerdal QCPR ResusciAnne manikin with the Multimodal Tutor for CPR, a multi-sensor system c...
A machine learning based model for Out of Hospital cardiac arrest outcome classification and sensitivity analysis.
Resuscitation
Mar 15, 2019
BACKGROUND: Out-of-hospital cardiac arrest (OHCA) affects nearly 400,000 people each year in the United States of which only 10% survive. Using data from the Cardiac Arrest Registry to Enhance Survival (CARES), and machine learning (ML) techniques, w...
Machine learning as a supportive tool to recognize cardiac arrest in emergency calls.
Resuscitation
Jan 18, 2019
BACKGROUND: Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus lose the opportunity to provide the caller instructions in cardiopulmonary resuscitation. We examined whether a machine lear...
Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study.
PLoS medicine
Nov 30, 2018
BACKGROUND: Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admi...
Registries
Aged
Retrospective Studies
New Zealand
Male
Humans
Clinical Decision-Making
Cardiopulmonary Resuscitation
Treatment Outcome
Middle Aged
Machine Learning
Female
Heart Arrest
Health Status
Risk Assessment
Risk Factors
Decision Support Techniques
Australia
Databases, Factual
Time Factors
Hospital Mortality
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