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.