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Updated: Jul 10, 2026

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
Automated detection of paroxysmal atrial fibrillation from inter-heartbeat intervals
Redmond B Shouldice1, Conor Heneghan, Philip de Chazal
1BiancaMed, NovaUCD, University College Dublin, Belfield, Dublin 4, Ireland. redmond.shouldice@biancamed.com
Abstract:
An automated method for detecting episodes of probable paroxysmal atrial fibrillation based on processing blocks of inter-heartbeat intervals is considered. The method has very low computational requirements making it well-suited to near real-time, low power applications. A supervised linear discriminant classifier is used to estimate the likelihood of a block of inter-heartbeat intervals containing paroxysmal atrial fibrillation (PAF). Per block accuracies in separating normal from PAF were 92%, 94%, 100% and 100% when the method was used to process the Physionet MITDB, AFDB, NSRDB and NSR2DB databases respectively.
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