Enhancing the accuracy of shock advisory algorithms in automated external defibrillators during ongoing cardiopulmonary resuscitation using a cascade of CNNEDs

Delivery of continuous cardiopulmonary resuscitation (CPR) plays an important role in the out-of-hospital cardiac arrest (OHCA) survival rate. However, to prevent CPR artifacts being superimposed on ECG morphology data, currently available automated external defibrillators (AEDs) require pauses in C...

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Vydané v:	Computers in biology and medicine Ročník 172; s. 108180
Hlavní autori:	Nejad, Mahdi Pirayesh Shirazi, Kargin, Vadym, Hajeb-M, Shirin, Hicks, David, Valentine, Matt, Chon, K.H.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Ltd 01.04.2024 Elsevier Limited
Predmet:	Accuracy Algorithms Arrhythmias, Cardiac - diagnosis Artificial neural networks Automation Cardiac arrest Cardiac arrhythmia Cardiopulmonary resuscitation Cardiopulmonary Resuscitation - methods Classification Contamination Convolutional Neural Network CPR Decision analysis Decision making Defibrillators Defibrillatory shock EKG Electrocardiogram Electrocardiography Electrocardiography - methods Encoder and decoder Encoders-Decoders Fibrillation Heart rate Humans Internal Medicine Lifesaving Neural networks Other Performance evaluation Reference signals Reproducibility of Results Resuscitation Rhythm Segments Shock Signal processing Sudden cardiac arrest Tachycardia Tachycardia, Ventricular Test sets Ventricle Ventricular Fibrillation Ventricular tachycardia Sudden cardiac arrest Defibrillatory shock Convolutional Neural Network Encoder and decoder Cardiopulmonary resuscitation Electrocardiogram Ventricular tachycardia Ventricular fibrillation
ISSN:	0010-4825, 1879-0534, 1879-0534
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