Automated Assessment of Atrial Fibrillation

This study presents our entry in this year’s Computers in Cardiology challenge. The challenge is the automated assessment of the electrocardiogram for predicting the onset of paroxysmal atrial fibrillation/flutter (PAF). By considering a large set of features derived from RR intervals, P wave shape and frequency representations of the P wave we compared the performance of a linear discriminant classifier processing the feature sets. The cross-validation scheme was used to estimate classifier performance. Results demonstrated that features based on RR intervals were the most successful. Independent testing showed that the ECG may be of some potential use in screening subjects for PAF and predicting the onset of PAF.