Open AccessArtificial intelligence-enabled electrocardiogram to distinguish cavotricuspid isthmus dependence from other atrial tachycardia mechanisms
Author(s) -
Arunashis Sau,
Safi Ibrahim,
Amar Ahmed,
Balvinder S. Handa,
Daniel B. Kramer,
Jonathan W. Waks,
Ahran Arnold,
James P. Howard,
Norman Qureshi,
Michael KoaWing,
Daniel Keene,
Louisa MalcolmeLawes,
David Lefroy,
Nick Linton,
Phang Boon Lim,
Amanda Varnava,
Zachary I. Whinnett,
Prapa Kanagaratnam,
Danilo P. Mandic,
Nicholas S. Peters,
Fu Siong Ng
Publication year - 2022
Publication title -
european heart journal - digital health
Language(s) - English
Resource type - Journals
ISSN - 2634-3916
DOI - 10.1093/ehjdh/ztac042
Subject(s) - medicine , atrial flutter , atrial tachycardia , cardiac electrophysiology , cardiology , ablation , tachycardia , convolutional neural network , electrocardiography , artificial intelligence , gold standard (test) , catheter ablation , electrophysiology , computer science
Accurately determining atrial arrhythmia mechanisms from a 12-lead electrocardiogram (ECG) can be challenging. Given the high success rate of cavotricuspid isthmus (CTI) ablation, identification of CTI-dependent typical atrial flutter (AFL) is important for treatment decisions and procedure planning. We sought to train a convolutional neural network (CNN) to classify CTI-dependent AFL vs. non-CTI dependent atrial tachycardia (AT), using data from the invasive electrophysiology (EP) study as the gold standard.
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