Cardiac Tachyarrhythmia Detection by Poincaré Plot-Based Image Analysis

Abstract

Tachyarrhythmia detection through RR interval analysis could improve performance of monitoring devices. In this paper a Poincaré plot-based image approach is presented. Three cardiac rhythms were analyzed in this study: normal sinus rhythm (NSR), atrial fibrillation (AF) and atrial bigeminy (AB). Using different MIT-BIH databases, 27955, 3363 and 76 images were generated for NSR, AF and AB respectively using a 2-minute window with 50 % overlap. The 80 % of the data available for each rhythm was used to create a reference rhythm image atlas. The remaining 20 % was classified into one of the three categories using mutual information. The process was iterated 10 times, in which images used to construct the atlas and used to create the test set were randomly selected. AF was correctly classified 94.12 % ± 0.45, AB 72.00 % ± 11.24 and NSR 80.70 %±0.54. The results of the present study suggest that Poincaré plot-based image analysis is a promising path for classifying different rhythms using only ventricular activity.