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Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest
(MDPI, 2019-03-21)
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 (end of the arrest). The only signal ...
A Multistage Algorithm for ECG Rhythm Analysis During Piston-Driven Mechanical Chest Compressions
(IEEE, 2019-01)
Goal: An accurate rhythm analysis during cardiopulmonary resuscitation (CPR) would contribute to increase the survival from out-of-hospital cardiac arrest. Piston-driven mechanical compression devices are frequently used ...
A Machine Learning Shock Decision Algorithm for Use During Piston-Driven Chest Compressions
(IEEE, 2019-06)
Goal: Accurate shock decision methods during piston-driven cardiopulmonary resuscitation (CPR) would contribute to improve therapy and increase cardiac arrest survival rates. The best current methods are computationally ...
Capnography: A support tool for the detection of return of spontaneous circulation in out-of-hospital cardiac arrest
(Elsevier, 2019-09)
Background
Automated detection of return of spontaneous circulation (ROSC) is still an unsolved problem
during cardiac arrest. Current guidelines recommend the use of capnography, but most
automatic methods are based ...
ECG-based pulse detection during cardiac arrest using random forest classifier
(Springer, 2019)
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 spontaneous circulation during therapy, ...
Automatic Cardiac Rhythm Classification with Concurrent Manual Chest Compressions
(IEEE, 2019-08-13)
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 on patient response.
OHCA rhythms are ...