Automated detection of paroxysmal atrial fibrillation from inter-heartbeat intervals.
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.
Shouldice
Redmond B
RB
BiancaMed, NovaUCD, University College Dublin, Belfield, Dublin 4, Ireland. redmond.shouldice@biancamed.com
Heneghan
Conor
C
de Chazal
Philip
P
D001281:Atrial Fibrillation / Q000175:diagnosis / Q000503:physiopathology*; D016208:Databases, Factual; D004562:Electrocardiography / Q000379:methods; D001330:Electronic Data Processing / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008955:Models, Cardiovascular