Poincaré plot image and rhythm-specific atlas for atrial bigeminy and atrial fibrillation detection

• Main Authors: Corino, V. (Author), Garcia-Isla, G. (Author), Mainardi, L. (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Subjects: 2D correlation; algorithm; Algorithms; Article; atrial bigeminy; atrial bigeminy (AB); atrial fibrillation; Atrial fibrillation; Atrial Fibrillation; Atrial fibrillation (AF); Biomedical signal processing; Cardiac monitoring; Classification methods; controlled study; cross validation; Cross validation; Databases, Factual; diagnostic accuracy; diagnostic test accuracy study; Different time windows; Diseases; electrocardiography; Electrocardiography; entropy; factual database; heart atrium arrhythmia; heart rate; Heart Rate; heart rhythm; human; Humans; image analysis; mathematical analysis; mathematical model; Monitoring, Physiologic; Normal sinus rhythm; Normalized mutual information; normalized mutual information (NMI); Open source database; physiologic monitoring; rhythm classification; RR interval; sensitivity and specificity; sinus rhythm
• Online Access: View Fulltext in Publisher

 A detector based only on RR intervals capable of classifying other tachyarrhythmias in addition to atrial fibrillation (AF) could improve cardiac monitoring. In this paper a new classification method based in a 2D non-linear RRI dynamics representation is presented. For this aim, the concepts of Poincaré Images and Atlases are introduced. Three cardiac rhythms were targeted: Normal sinus rhythm (NSR), AF and atrial bigeminy (AB). Three Physionet open source databases were used. Poincaré Images were generated for all signals using different Poincaré plot configurations: RR, dRR and RRdRR. The study was computed for different time window lengths and bin sizes. For each rhythm, the Poincaré Images of the 80% of that rhythm's patients were used to create a reference image, a Poincaré Atlas. The remaining 20% were used as test set and classified into one of the three rhythms using normalized mutual information and 2D correlation. The process was iterated in a tenfold cross-validation and patient-wise dataset division. Sensitivity results obtained for RRdRR configuration and bin size 40 ms, for a 60 s time window were 94.35% ±3.68, 82.07% ±9.18 and 88.86% ±12.79 with a specificity of 85.52% ±7.46, 95.91% ±3.14, 96.10% ±2.25 for AF, NSR and AB respectively. Results suggest that a rhythms general RRI pattern may be captured using Poincaré Atlases and that these can be used to classify other signal segments using Poincaré Images. In contrast with other studies, the former method could be generalized to more cardiac rhythms and does not depend on rhythm-specific thresholds. © 2013 IEEE.