PO-05-229 DEEP LEARNING CLASSIFICATION OF PAROXYSMAL SUPRAVENTRICULAR TACHYCARDIA TYPES USING SINUS RHYTHM ELECTROCARDIOGRAMS

Abstract

Only sinus rhythm electrocardiograms (ECGs) are often available in patients with paroxysmal supraventricular tachycardia (PSVT). However, sinus rhythm ECGs have a limited value in classifying PSVT types. We aimed to investigate the deep learning approach of classifying Atrioventricular Nodal Reentry Tachycardia (AVNRT) from concealed Atrioventricular Reentry Tachycardia (AVRT) using sinus rhythm ECGs. We collected the patients with AVNRT or conceald AVRT, and their 12-lead ECGs with sinus rhythm. The diagnosis of each patient was validated with an electrophysiology study. ResNet-34 was used for the deep learning model, and it was trained to classify sinus rhythm ECGs with underlying AVNRT and conceald AVRT. Due to the limited training dataset, transfer learning was performed by pre-training of an open-source ECG database (PhysioNet/CinC Challenge 2021). Ten-fold cross-validation was used to validate deep learning performance. Integrated gradient analysis was used to visualize which ECG segment is important for the model to classify the two arrhythmias. A total of 696 patients with AVNRT and 305 patients with conceald AVRT were analyzed. Compared to the AVNRT group, the conceald AVRT group was significantly younger and had a higher male proportion; age 48 vs. 51 years (p<0.001); male 63.3 vs. 34.3% (p<0.001). After the pre-training, the deep learning performance was significantly improved; AUROC 0.73 (95% CI 0.69-0.76) vs. 0.67 (0.62-0.72) for with and without pre-training; p<0.001 (Figure A). Deep learning showed modest performance for classifying the two arrhythmias; AUPRC, F1-score, sensitivity, and specificity were 0.84 (0.81-0.87), 0.75 (0.70-0.80), 0.68 (0.61-0.75), and 0.71 (0.65-0.77), respectively. Deep learning focused on PR intervals and T waves for identifying AVNRT, and P-waves for concealed AVRT (Figure B). Deep learning classification of AVNRT and conceald AVRT was feasible with sinus rhythm ECGs.