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 have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, dee...

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Veröffentlicht in:	PloS one 2019-05, Vol.14 (5), p.e0216756-e0216756
Hauptverfasser:	Picon, Artzai, Irusta, Unai, Álvarez-Gila, Aitor, Aramendi, Elisabete, Alonso-Atienza, Felipe, Figuera, Carlos, Ayala, Unai, Garrote, Estibaliz, Wik, Lars, Kramer-Johansen, Jo, Eftestøl, Trygve
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Architecture Arrhythmia Artificial neural networks Cardiac arrest Cardiopulmonary resuscitation Classifiers Computer and Information Sciences Computer architecture CPR Databases, Factual - statistics & numerical data Datasets Deep Learning Defibrillators Defibrillators - statistics & numerical data Diagnosis Diagnosis, Computer-Assisted - methods Diagnosis, Computer-Assisted - statistics & numerical data EKG Electric countershock Electric Countershock - methods Electric Countershock - statistics & numerical data Electrocardiography Electrocardiography - statistics & numerical data Electrocardiography, Ambulatory - statistics & numerical data Emergency medical care Feature extraction Fibrillation Heart Humans Learning algorithms Long short-term memory Machine learning Medicine and Health Sciences Memory, Short-Term Neural networks Neural Networks, Computer Out-of-Hospital Cardiac Arrest - diagnosis Out-of-Hospital Cardiac Arrest - therapy Patient outcomes Physical Sciences Repositories Research and Analysis Methods Shock Signal Processing, Computer-Assisted Support Vector Machine Support vector machines Technology application Ventricle Ventricular fibrillation Ventricular Fibrillation - diagnosis
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