Poincaré Plot Image and Rhythm-Specific Atlas for Atrial Bigeminy and Atrial Fibrillation Detection

Abstract

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 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 patientwise 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 rhythm's 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.

A Poincaré Image-Based Detector of ECG Segments Containing Atrial and Ventricular Beats

“…The Poincaré Plot is a graphic representation of one RR i (or dRR i ) plotted against the previous one, RR i−1 (or dRR i−1 ). Poincaré Images were computed by binarizing the space of the Poincaré Plot for both RR and dRR as in [6]. Following the study in [6] the bin size was set to 40 ms and the Poincaré Images ranges were set to [0, 1600] ms and [-800, 800] ms in both x and y-axis for RR and dRR images, respectively.…”

“…Poincaré Images were computed by binarizing the space of the Poincaré Plot for both RR and dRR as in [6]. Following the study in [6] the bin size was set to 40 ms and the Poincaré Images ranges were set to [0, 1600] ms and [-800, 800] ms in both x and y-axis for RR and dRR images, respectively. Poincaré Images containing at least one beat labelled as A/V were labelled as A/V whereas those not containing any beat of the mentioned categories were labelled as N .…”

“…This work presents an automatic A/V ECG segment classifier based on the Poincaré Image representation of RR intervals as described in [6] and convolutional neural networks (CNN). The presented classifier enables the detection of regions containing A/V beats, drastically reducing the workload for manual beat annotations.…”