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An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction
Zachi I Attia ,
Peter A Noseworthy ,
Francisco Lopez-Jimenez ,
Samuel J Asirvatham ,
Abhishek J Deshmukh ,
Bernard J Gersh ,
Rickey E Carter ,
Xiaoxi Yao ,
Alejandro A Rabinstein ,
Brad J Erickson ,
Suraj Kapa ,
Paul A Friedman
August 2019
August 2019
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated
with stroke, heart failure, and death. Existing screening methods require prolonged
monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive,
point-of-care means of identifying patients with atrial fibrillation using machine
learning.