A morphology based deep learning model for atrial fibrillation detection using single cycle electrocardiographic samples

Author:

Baalman Sarah W.E.,Schroevers Florian E.,Oakley Abel J.,Brouwer Tom F.,van der Stuijt Willeke,Bleijendaal Hidde,Ramos Lucas A.,Lopes Ricardo R.,Marquering Henk A.,Knops Reinoud E.,de Groot Joris R.

Funder

Boston Scientific

Medtronic

Abbott

Dutch Organization for Scientific Research

AtriCure

Publisher

Elsevier BV

Subject

Cardiology and Cardiovascular Medicine

Reference27 articles.

1. 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS;Kirchhof;Eur. Heart J.,2016

2. Errors in the computerized electrocardiogram interpretation of cardiac rhythm;Shah;J. Electrocardiol.,2007

3. Comparative study of algorithms for atrial fibrillation detection,2011

4. A deep learning approach for real-time detection of atrial fibrillation;Andersen;Expert Syst. Appl.,2019

5. A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology,2017

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