Paroxysmal Atrial Fibrillation diagnosis based on feature extraction and classification

Abstract

Paroxysmal Atrial Fibrillation (PAF), a really life threatening disease, is the result of irregular and repeated depolarization of the atria. In this paper, patients with PAF disease and their different episodes can be detected by extracting statistical and morphological features from ECG signals and classifying them by applying artificial neural network (ANN), Bayes optimal classifier and K-nearest neighbor (k-NN) classifier. Consequently, we become successful to diagnose about 93% of PAF patients among healthy cases and also detect their ECG signal different episodes such as those far from the PAF episode and the ones which are immediately before PAF episode with the correct classification rates (CCR) of more than 90%.