Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/44671
Title: Predicting paroxysmal atrial fibrallation in cryptogenic stroke patients from an electrocardiogram in sinus rhythm using artificial intelligence
Authors: WOUTERS, Femke 
GRUWEZ, Henri 
BARTHELS, Myrte 
MESOTTEN, Dieter 
Verhaert, David
PISON, Laurent 
Ernon, Ludovic
HAEMERS, Phoebe 
NUYENS, Dieter 
DHONT, Sebastiaan 
SMEETS, Christophe 
VRANKEN, Julie 
VANDERVOORT, Pieter 
Issue Date: 2024
Publisher: ELSEVIER SCIENCE INC
Source: Journal of the American College of Cardiology, 83 (13) , p. 2447
Abstract: Background: Artificial intelligence (AI)-enabled ECG algorithms can predict underlying paroxysmal AF from ECGs recorded during sinus rhythm within a 30-day window. Their predictive value in cryptogenic stroke patients over a 1-year duration is unknown.
Document URI: http://hdl.handle.net/1942/44671
ISSN: 0735-1097
e-ISSN: 1558-3597
ISI #: 001324901502481
Category: M
Type: Journal Contribution
Appears in Collections:Research publications

Files in This Item:
File Description SizeFormat 
PREDICTING PAROXYSMAL ATRIAL FIBRILLATION IN CRYPTOGENIC STROKE PATIENTS FROM AN ELECTROCARDIOGRAM .pdfPublished version362.75 kBAdobe PDFView/Open
Show full item record

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.