Abstract The aim of this work is to develop a method for detection of brief episode paroxysmal atrial fibrillation (PAF Introduction The detection of brief episode paroxysmal atrial fibrillation (PAF) is an important problem to solve since atrial fibrillation (AF) is a progressive disorder. If not treated, PAF usually becomes more frequent and longer until it becomes permanent Automatic AF detection can be done in different waysone is based on identification of P-wave absence and another on the analysis of RR interval irregularity. Since P-waves are not apparent during AF such knowledge can be combined with RR irregularity information in order to improve the performance of AF detection Recently, there has been a growing interest in developing algorithms for detection of brief AF episodes. A sample entropy based method was proposed that is capable of detecting AF using only 12 consecutive RR intervals A novel detector architecture was recently proposed, where information on P wave presence/absence, heart rate irregularity, and atrial activity analysis was combined, using an artificial neural network as classifier In this study, the proposed method is based on atrial activity extraction using an echo state network (ESN) recently introduced as a unified solution to the problem of QRST cancellation in the presence of substantial variation in beat morphology and/or occasional ectopic beats Methods The main processing steps of the proposed AF detector are illustrated i
