Deep neural networks for ECG-based pulse detection during out-of-hospital cardiac arrest
The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary
for the early recognition of the arrest and the detection of return of spontaneous circulation …
for the early recognition of the arrest and the detection of return of spontaneous circulation …
ECG-based classification of resuscitation cardiac rhythms for retrospective data analysis
Objective: There is a need to monitor the heart rhythm in resuscitation to improve treatment
quality. Resuscitation rhythms are categorized into: ventricular tachycardia (VT), ventricular …
quality. Resuscitation rhythms are categorized into: ventricular tachycardia (VT), ventricular …
Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia
Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-
of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning …
of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning …
Ventricular fibrillation waveform analysis during chest compressions to predict survival from cardiac arrest
Background: Quantitative measures of the ventricular fibrillation (VF) ECG waveform can
assess myocardial physiology and predict cardiac arrest outcomes, making these measures …
assess myocardial physiology and predict cardiac arrest outcomes, making these measures …
ECG-based pulse detection during cardiac arrest using random forest classifier
Sudden cardiac arrest is one of the leading causes of death in the industrialized world.
Pulse detection is essential for the recognition of the arrest and the recognition of return of …
Pulse detection is essential for the recognition of the arrest and the recognition of return of …
Multimodal algorithms for the classification of circulation states during out-of-hospital cardiac arrest
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 …
essential to determine what life-saving therapies to apply. Currently algorithms discriminate …
Capnography: A support tool for the detection of return of spontaneous circulation in out-of-hospital cardiac arrest
Background Automated detection of return of spontaneous circulation (ROSC) is still an
unsolved problem during cardiac arrest. Current guidelines recommend the use of …
unsolved problem during cardiac arrest. Current guidelines recommend the use of …
Automatic cardiac rhythm classification with concurrent manual chest compressions
Electrocardiogram (EKG) based classification of out-of-hospital cardiac arrest (OHCA)
rhythms is important to guide treatment and to retrospectively elucidate the effects of therapy …
rhythms is important to guide treatment and to retrospectively elucidate the effects of therapy …
A machine learning model for the prognosis of pulseless electrical activity during out-of-hospital cardiac arrest
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 …
and electrical activity of the heart and appears as the initial rhythm in 20–30% of out-of …
Predicting ROSC in out-of-hospital cardiac arrest using expiratory carbon dioxide concentration: Is trend-detection instead of absolute threshold values the key?
P Brinkrolf, M Borowski, C Metelmann, RP Lukas… - Resuscitation, 2018 - Elsevier
Aim Guidelines recommend detecting return of spontaneous circulation (ROSC) by a rising
concentration of carbon dioxide in the exhalation air. As CO 2 is influenced by numerous …
concentration of carbon dioxide in the exhalation air. As CO 2 is influenced by numerous …