Abstract

Atrial fibrillation (AF) is an arrhythmic behaviour of the heart, which occurs when the myocardium of the atrial chambers enter into a sustained chaotic and fractionated muscular contraction dynamic. Reliable detection of AF episodes in ECG monitoring devices, is important for early treatment and health risks reduction. A decision rule for identifying AF arrhythmic patterns was derived from RR-intervals analysis of time-seriesgenerated from ECG recordings before, during and after AF episodes. Time-series elements were obtained by consecutive RR intervals time differences (dRR). In theproposed decision rule, two arguments must be satisfied for identifying an AF pattern within a window of 35 beats: (1) the number of dRR elements above 50 msabsolute value, is >10, and (2) there is a uniform dispersion of all the corresponding RR-interval elements within the same 35 beat window. Detection of AF using the proposed decision rule scheme was achieved with 96% exactitude, 93% sensitivity and 97% specificity. The longest case of processing time per ECG beat was of 129ms. Thiscomputing time requirement can enable real-time ECG processing algorithms for AF identification.