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

Abstract

An electrocardiogram (ECG) classifier for the detection of ECG segments containing atrial or ventricular (A/V) beats could ease in the detection of premature atrial complexes (PACs) and by so, the study of their relationship with atrial fibrillation (AF) and stroke. In this work such a classifier is presented based on convolutional neural networks (CNN) and the RR and dRR interval representation on Poincaré Images. Two PhysioNet opensource databases containing beat annotations were used. ECG signals were divided into 30-beat segments with a 50% overlap. Each segment was then transformed into a Poincaré Image. A total of 381151 and 62142 Poincaré Images were computed for normal (N) and A/V segments. RR, dRR and both types of Poincaré Images combined were evaluated as inputs to the CNN. The CNN was trained following a patient-wise train-test division (i.e., no patient was included both in the train and test set) in a 10-fold cross-validation. The patient-wise median and interquartile range accuracy, sensitivity and positive predictive values were 97.90 (94.49 -99.28), 96.03 (89.67 -98.76) and 91.91 (70.87 -99.24), respectively for RR input. No statistical significant differences in performance were found among the three types of Poincaré Images input. Results suggest the present methodology manages to distinguish among N and A/V with high precision.